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About This Role
At Johnson \& Johnson, we believe health is everything. Our strength in healthcare innovation empowers us to build a world where complex diseases are prevented, treated, and cured, where treatments are smarter and less invasive, and solutions are personal. Through our expertise in Innovative Medicine and MedTech, we are uniquely positioned to innovate across the full spectrum of healthcare solutions today to deliver the breakthroughs of tomorrow, and profoundly impact health for humanity. Learn more at jnj.com
As guided by Our Credo, Johnson \& Johnson is responsible to our employees who work with us throughout the world. We provide an inclusive work environment where each person is considered as an individual. At Johnson \& Johnson, we respect the diversity and dignity of our employees and recognize their merit.
Job Function:
Technology Product \& Platform ManagementJob Sub Function:
Multi\-Family Technology Product \& Platform ManagementJob Category:
People LeaderAll Job Posting Locations:
Raritan, New Jersey, United States of AmericaJob Description:
The Vice President of Data \& AI Platforms is a senior technology executive responsible for transforming Johnson \& Johnson into a truly data driven, AI enabled enterprise. This leader defines and drives J\&J’s strategy for enterprise data platforms, advanced analytics infrastructure, AI/ML (including Generative AI) product development, and AI governance \& ethics. The VP’s mandate is to unlock the value of J\&J’s vast data assets through scalable platforms and innovative AI solutions that accelerate R\&D, enhance supply chain resilience, personalize customer engagement, and improve operational efficiency. A core part of this role is ensuring that trusted data and AI tools are broadly accessible across the company by partnering with Technology Services (TS) peers and business units (sectors/functions), J\&J Data Management Council to decentralize execution while maintaining strategic alignment. Importantly, this VP now also directly oversees J\&J’s internal Generative AI platforms and tools including the company’s “Intelligent Chat” enterprise chatbot, LLM API Marketplace, Content Sphere knowledge retrieval platform, Agentic AI workflow framework, Draft Assist document generator and Orchestration Platform and the GenAI Governance Portal. By advancing and scaling these capabilities under robust governance, the VP will shape how data and GenAI technologies power innovation and competitive advantage at J\&J, all while upholding Our Credo values and rigorous ethical standards. This position reports to the Global CTO and is a key member of the Technology Services Leadership Team, leading a global organization (\~100 staff) across data engineering, data integration, AI platforms, AI enablement, and data/AI governance. The role is based in Raritan, NJ, with a global remit and will require international travel to support worldwide teams and initiatives
Key Responsibilities
- Enterprise Data Platforms \& Quality:
+ Own end\-to\-end data engineering and platform operations for J\&J’s enterprise data ecosystem.
+ Manage the design, development, and 24x7 operation of scalable, secure, and AI\-ready data pipelines that ingest and integrate data from all key domains (R\&D, Commercial, Supply Chain, Finance, etc.) into a modern, cloud\-based data framework.
+ Ensure high\-quality, well\-governed data by driving timely ingestion, transformation, and reconciliation of both structured and unstructured data across the company. This involves championing robust data management practices (data cleansing, master data management, metadata cataloging) so that J\&J’s data is “single\-source\-of\-truth” and ready for analytics/AI usage.
+ Success in this area means J\&J’s analysts and data scientists spend far less time on data prep and trust the accuracy of data (e.g. targeting \>50% reduction in data preparation effort through better data quality and integration).
+ The VP also aligns data platform architecture with Enterprise Architecture standards and Security/Privacy requirements – embedding compliance with HIPAA, GDPR, and internal policies into all data processes. In summary, this leader ensures J\&J’s data foundations are strong, reliable, and primed for AI, enabling faster insights and better business decisions globally
- Data Platform \& Architecture Ownership:
+ Oversee the design and operation of J\&J’s core data stores and infrastructures, including cloud\-based data lakes, data warehouses, and emerging GenAI/“Agentic AI” platforms.
+ As the top data architect, champion modern, scalable data architecture paradigms – for example, implementing a “data lakehouse” approach that combines the flexibility of data lakes with the structure of warehouses, leveraging streaming data pipelines for real\-time needs, and exploring data fabric concepts for unified data access.
+ This VP owns the reference architecture for data and AI systems: setting technology standards and design patterns that guide how data platforms and AI solutions are built across J\&J. That includes selecting and integrating the right cloud technologies, database systems, big data tooling, and ensuring these components work together seamlessly.
+ A key focus is integrating GenAI and analytics into the architecture – e.g. making sure that the infrastructure supports large\-scale training/inference, providing vector databases and knowledge graphs to power LLM applications.
+ The VP continuously evaluates and upgrades the architecture for performance, cost\-efficiency, and scalability. For instance, in 2026 the strategy may involve further consolidation of on\-premise data warehouses into cloud platforms, deploying serverless or auto\-scaling technologies to handle growth efficiently, and ensuring network/connectivity aspects (in partnership with Infrastructure) are robust for global data flows.
+ Ultimately, the VP’s accountability is a future\-ready data platform architecture that can accommodate the explosion of data and AI workloads, while remaining secure and compliant
- GenAI \& AI Platforms and Product Development:
+ Lead the development and scaling of J\&J’s AI/ML platforms and Generative AI solutions, serving both technical teams and end\-users. This includes building and managing enterprise\-wide platforms for machine learning operations (MLOps) – ensuring robust model lifecycle management from experimentation to deployment to monitoring.
+ The VP oversees the creation of orchestration layers and integration frameworks that allow AI models and algorithms to plug into business processes and applications. This leader owns J\&J’s internal Generative AI platform products (originally incubated by TS Architecture/GenAI team) and will drive their evolution and broad adoption. These include the Intelligent Chat chatbot, the LLM API Marketplace (a one\-stop API layer for accessing various large language models across Azure OpenAI, AWS Bedrock, etc.), the Content Sphere retrieval\-augmented generation platform (enabling AI Q\&A against enterprise documents and knowledge), the Agentic AI Framework (a low\-code toolkit to develop AI agents and workflows), and Draft Assist (an AI\-powered document drafting and content generation tool). The VP also stewards the GenAI Governance Portal (GovernAI), which manages AI use\-case approvals and compliance workflows. By consolidating these GenAI products under one umbrella, the VP ensures they are secure, scalable, and delivering value. Key tasks include enhancing these platforms with new capabilities (for example, integrating the latest GPT\-5/Claude models as they become available, enabling multi\-cloud deployments for resilience, and improving features like “digital front door” unified chat access), while also optimizing performance and cost (e.g. managing the Provisioned Throughput Units for LLM hosting to meet demand efficiently).
+ The VP will work closely with the VP of End user services to align the broader AI strategy on CoPilot Premium including determining the future of J\&J Intelligent Chat.
+ The VP is accountable for the performance, quality, and compliance of all AI solutions and platforms in J\&J, from advanced analytics models to generative AI services. This means instituting rigorous testing/validation for AI products, monitoring model outcomes and system usage, and quickly addressing any issues in output quality or reliability. By providing world\-class AI and GenAI platforms, this leader enables data scientists, engineers, and even citizen developers across J\&J to rapidly develop AI solutions that are production\-grade, thus significantly accelerating J\&J’s overall AI innovation.
- Data Services \& Integration:
+ Deliver enterprise data services and integration capabilities that make J\&J’s data both accessible and actionable throughout the business. This responsibility covers a broad set of data\-centric services, including enterprise data integration platforms (to connect and transform data between systems), data APIs and microservices, and business intelligence (BI) and analytics tools.
+ The VP’s team provides reliable data integration pipelines and enterprise integration services that allow various applications (ERP, CRM, lab systems, etc.) to share data seamlessly, reducing silos. Additionally, this leader ensures J\&J has robust self\-service analytics and BI platforms – such as the governance and support of tools like Tableau, Power BI or other visualization environments – so that business users and analysts can easily access and analyze data without heavy IT involvement.
+ The VP drives initiatives to improve the usability and reach of data services: for example, establishing a data marketplace or catalog that lets employees discover available data sets, or creating standardized data products (like common data models for customer or product data) that multiple teams can leverage. A key goal is to democratize data access and insights across the enterprise: success is measured by increased adoption of self\-service data tools, reduced cycle time for analytics (e.g., automatically generating certain reports with AI assistance), and high satisfaction from business stakeholders who feel their data needs are met quickly.
+ The VP also defines and tracks KPIs for data service performance and usage – such as data pipeline reliability (uptime, latency), number of active users of analytics tools, and turnaround time for integration requests – and implements continuous improvements. By delivering robust data services, this leader ensures that data is readily available and consumable where and when it’s needed, which is the foundation for all advanced analytics and AI initiatives.
- AI Governance, Risk \& Ethics:
+ In partnership with the GenAI Governance Board, act as J\&J’s ve steward for responsible AI, ensuring that all Gen AI activities adhere to the highest standards of ethics, quality, and compliance. The VP chairs the GenAI Governance Council and is a key member of broader data/AI governance bodies (such as J\&J’s Data Management Council and AI Council).
+ In this role, implement and enforce enterprise\-wide AI governance policies – covering areas like bias mitigation, model validation, transparency, and regulatory compliance. For example, the VP will oversee processes to test AI models for bias or errors before deployment, to validate models especially in high\-risk use cases (like GxP\-regulated manufacturing or clinical decision support), and to ensure privacy and data protection for any AI that uses sensitive data.
+ This leader’s team maintains a central inventory of Gen AI models and algorithms in use across J\&J, along with their intended purpose, training data, and risk assessments; laying the groundwork for model lifecycle governance. They also manage the GenAI use\-case review process via the GovernAI portal, streamlining approvals while capturing necessary oversight (e.g., the introduction of a “Fast Track” for low\-risk AI experiments which now approves \~25% of use cases instantly).
+ The VP ensures the GenAI Governance process stays ahead of emerging complexities for instance, as new AI paradigms like autonomous agents become more common, governance criteria and technical guardrails are updated accordingly. Success in governance means that J\&J avoids major AI\-related incidents or ethical lapses: no unauthorized use of protected data, no uncontrolled “rogue” AI deployments, and no reputational damage from AI outcomes. Additionally, this VP champions explainable and transparent AI, working with technical teams to implement features like model explainability reports or bias dashboards so that business leaders and regulators can understand AI\-driven decisions. By fostering a culture of “AI with integrity,” the VP helps maintain stakeholder trust and aligns J\&J’s AI push with its Credo and compliance obligations.
- Enterprise AI Enablement \& Adoption (Strategy \& Oversight):
+ Partner with JJT Strategy and Operations team in driving enterprise\-wide AI enablement and literacy to ensure J\&J’s businesses successfully adopt AI solutions at scale.
+ This team co\-owns the AI/GenAI enablement strategy in partnership with the TS Strategy \& Operations team, but critically, allows sectors and functions to lead execution of AI use cases in their domains (a federated model). This leader’s role is to provide the common platforms, frameworks, and training that empower those distributed teams to implement AI effectively. Together they provide toolkits and best\-practice playbooks for AI project execution, covering how to identify good AI opportunities, how to build with J\&J’s GenAI tools, how to measure benefits, etc..
+ The VP also coordinates a federated “AI Community of Practice” connecting data scientists, AI product owners, and business analysts across sectors. Through this community, they share successful use cases, avoid duplication of efforts (e.g., preventing multiple teams from building similar NLP solutions by encouraging reuse of one platform or model), and surface needs that the central team can support.
+ The VP regularly tracks and reports on AI adoption metrics: for instance, what percentage of business units have deployed at least one AI solution, or what proportion of employees are actively using the “AI for All” productivity tools (the target is to reach \>70% of employees using AI tools monthly by end of 2026\). They intervene to address adoption barriers – whether technical (scaling issues), skill gaps, or cultural resistance partnering with HR and change management teams to do so. In summary, the VP acts as J\&J’s AI evangelist and enabler, making sure the great tools and data are actually put to use in delivering business outcomes, and that the workforce is both excited about and capable of leveraging AI day\-to\-day
- Leadership \& Organizational Management:
+ Provide strong vision and management for a global Data \& AI Platforms organization (\~100 professionals) that spans multiple continents and disciplines.
+ Directly oversee senior leaders and directors in areas including Data Engineering \& Platforms, Data Services \& Integration, AI Platforms \& Engineering, AI Enablement \& Performance, and Data \& AI Governance (per the 2026 TS org design).
+ This VP must establish a clear organizational structure with well\-defined roles and foster a culture of innovation, accountability, and inclusion. The VP champions talent development: attract and retain top\-tier talent in engineering and AI/Gen AI skills by promoting J\&J’s exciting mission (e.g., applying AI to improve patient outcomes).
+ Invest in upskilling internal talent – for instance, training traditional data engineers in cloud\-based AI tools, or converting business analysts into citizen data scientists through mentorship and coursework. Inclusion is emphasized in hiring and team culture, recognizing that diverse teams produce more robust, unbiased AI solutions. The VP ensures the team stays highly engaged and motivated by providing growth opportunities and recognition for accomplishments.
+ As part of TS leadership team, this role also involves cross\-functional collaboration and contributing to overall IT strategy: the VP works closely with peers in Infrastructure, Security, Enterprise Architecture, and Business Technology groups to align initiatives and share resources.
+ This leader must be adept at managing a matrix environment, since many data \& AI team members will work day\-to\-day with business units or other IT teams. Performance management is key: set clear OKRs for each sub\-team (e.g., data platform uptime, number of AI use cases delivered, user adoption stats, etc.) and hold leaders accountable. Ultimately, this VP’s leadership will be reflected in a high\-performing, agile, and business\-focused Data/AI function that is viewed as one of the best in the industry, and in J\&J’s reputation as a leader in leveraging data and AI (e.g., speaking at conferences, case studies with tech partners).
- Performance Measurement \& Value Realization:
+ Ensure that data and AI initiatives deliver measurable business value and continuously improve over time. The VP establishes a strong value\-tracking framework for all significant Data/AI projects – partnering with Finance and business stakeholders to quantify outcomes in terms of the 3E’s (Efficiency, Effectiveness, and Experience) or direct ROI. This includes setting target KPIs at project start (e.g., “AI\-driven inventory optimization will reduce excess stock by 20%, saving $X million”) and then tracking actuals post\-implementation.
+ The VP’s team produces regular dashboards/reporting on the impact of AI across J\&J, such as aggregate efficiency gains. Notably, with the new GenAI platforms, track enterprise productivity improvements: for example, it’s estimated that J\&J’s general GenAI productivity tools (Intelligent Chat, Copilot, etc.) are yielding \>$80M per year in time savings/value by late 2025 – the VP will validate and expand on these figures as usage grows.
+ They also measure adoption and usage statistics (inputs to value) and use these metrics to make decisions – if a certain AI tool is under\-utilized but high value, invest in marketing it internally; if a certain project isn’t yielding as expected, investigate and course\-correct or terminate. The VP fosters a culture of data\-driven decision\-making within TS itself, using metrics to drive prioritization. Additionally, embed continuous improvement loops: results and user feedback from deployed solutions feed into the backlog for enhancements or next phases. The goal is transparency and accountability: the C\-suite and business leaders should have clear line of sight into what Data \& AI initiatives are doing for the company’s bottom line, and the VP stands ready to explain and optimize that performance.
+ Over a 1\-2 year horizon, this leader aims to show significant contributions of data/AI to J\&J’s strategic goals – e.g., cycle time reductions in drug development due to AI analysis, improved customer satisfaction via AI personalization, or cost avoidance through better forecasting. By rigorously measuring outcomes, the VP ensures J\&J’s investments in data and AI are justified and are maximized for impact.
- External Engagement \& Thought Leadership:
+ Represent J\&J as a thought leader in the global data and AI community.
+ The VP will engage externally to keep a pulse on industry innovation and to promote J\&J’s progress. This involves speaking at industry conferences, participating in technology leadership forums, and forging strategic partnerships. By maintaining visibility into emerging technologies and best practices, this leader ensures J\&J’s data \& AI strategy stays ahead of the curve. Additionally, their involvement in external circles can help in recruiting top talent and enhancing J\&J’s brand as a tech innovator.
+ In summary, the VP balances internal execution with an outward\-looking perspective, ensuring J\&J’s Data \& AI capabilities are both leading and evolving with the broader tech ecosystem
Qualifications
- 15\+ years of progressive IT experience, with significant time in executive data and analytics roles (e.g., Head of Data \& Analytics, Chief Data Officer, Head of AI/ML).
- Proven leadership in large\-scale, enterprise data platform implementations and advanced analytics/AI deployments delivering measurable business value.
- Deep expertise managing end\-to\-end data pipelines and platforms, including cloud data lakes, integration, warehousing, and analytics provisioning.
- Extensive experience leading AI/ML projects in production and operationalizing ML models at scale, with exposure to Generative AI and modern AI technologies (LLMs, NLP, automation).
- Demonstrated success in building or scaling enterprise AI capabilities, including infrastructure, governance, and cross\-functional adoption processes.
- Strong technical acumen in data engineering and AI, including cloud platforms (AWS, Azure, GCP), big data technologies (Spark, Hadoop), and AI/ML frameworks (TensorFlow, PyTorch).
- Expertise in data governance, security, and compliance, with the ability to uphold trust and ethical standards in data and AI.
- Strategic vision and business acumen, linking data/AI initiatives to business outcomes and communicating value to senior stakeholders.
- Exceptional leadership and people management, with experience leading Senior Leaders of large, global teams and driving organizational change.
- Execution\-focused, with a track record of delivering major data/AI programs on time and within budget, and strong financial management skills.
- Industry experience in healthcare, pharma, or medical devices preferred; experience in other large, regulated, innovation\-driven sectors valued.
- Hands\-on or oversight experience with Generative AI implementations and emerging AI technologies; thought leadership (publications, patents, conferences) a plus.
- Global/multicultural experience leading international teams and navigating diverse regulatory and cultural environments.
- 20% travel required
- 10 Years people management experience
Preferred Qualifications
- An advanced degree (Master’s or Ph.D. in a relevant technical field, or an MBA with a technology focus) is highly preferred given the strategic and technical breadth of this role
Johnson \& Johnson is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, age, national origin, disability, protected veteran status or other characteristics protected by federal, state or local law. We actively seek qualified candidates who are protected veterans and individuals with disabilities as defined under VEVRAA and Section 503 of the Rehabilitation Act.
Johnson \& Johnson is committed to providing an interview process that is inclusive of our applicants’ needs. If you are an individual with a disability and would like to request an accommodation, external applicants please contact us via https://www.jnj.com/contact\-us/careers , internal employees contact AskGS to be directed to your accommodation resource.
Required Skills:
Preferred Skills:
Business Continuity Management (BCM), Business Planning, Business Savvy, Computer Programming, Creating Purpose, Developing Others, Give Feedback, Human\-Computer Interaction (HCI), Inclusive Leadership, Leadership, Managing Managers, Platform as a Service (PaaS), Product Knowledge, Relationship Building, Software Development Management, Strategic Change, Technical Credibility, VisualizationsThe anticipated base pay range for this position is :
$199,000\.00 \- $366,850\.00
Additional Description for Pay Transparency:
Subject to the terms of their respective plans, employees are eligible to participate in the Company’s consolidated retirement plan (pension) and savings plan (401(k)).
This position is eligible to participate in the Company’s long\-term incentive program.
Subject to the terms of their respective policies and date of hire, employees are eligible for the following time off benefits:
Vacation –120 hours per calendar year
Sick time \- 40 hours per calendar year; for employees who reside in the State of Colorado –48 hours per calendar year; for employees who reside in the State of Washington –56 hours per calendar year
Holiday pay, including Floating Holidays –13 days per calendar year
Work, Personal and Family Time \- up to 40 hours per calendar year
Parental Leave – 480 hours within one year of the birth/adoption/foster care of a child
Bereavement Leave – 240 hours for an immediate family member: 40 hours for an extended family member per calendar year
Caregiver Leave – 80 hours in a 52\-week rolling period10 days
Volunteer Leave – 32 hours per calendar year
Military Spouse Time\-Off – 80 hours per calendar year
Additional information can be found through the link below.
Co\-Ops and Intern Positions: Please use the following language:
Co\-Ops/Interns are eligible to participate in Company sponsored employee medical benefits in accordance with the terms of the plan.
Co\-Ops and Interns are eligible for the following sick time benefits: up to 40 hours per calendar year; for employees who reside in the State of Washington, up to 56 hours per calendar year.
Co\-Ops and Interns are eligible to participate in the Company’s consolidated retirement plan (pension).
Salary Context
This $199K-$366K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $100K across 15465 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 26,159 AI roles we're tracking, AI/ML Engineer positions make up 91% of the market. At Johnson & Johnson, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $166,983 based on 13,781 positions with disclosed compensation. This role's midpoint ($282K) sits 69% above the category median. Disclosed range: $199K to $366K.
Across all AI roles, the market median is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Architect ($292,900). By seniority level: Entry: $76,880; Mid: $131,300; Senior: $227,400; Director: $244,288; VP: $234,620.
Johnson & Johnson AI Hiring
Johnson & Johnson has 29 open AI roles right now. They're hiring across AI/ML Engineer. Positions span San Antonio, TX, US, Spring House, PA, US, Santa Clara, CA, US. Compensation range: $106K - $401K.
Location Context
Across all AI roles, 7% (1,863 positions) offer remote work, while 24,200 require on-site attendance. Top AI hiring metros: Los Angeles (1,695 roles, $178,000 median); New York (1,670 roles, $200,000 median); San Francisco (1,059 roles, $244,000 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 26,159 open positions tracked in our dataset. By seniority: 2,416 entry-level, 16,247 mid-level, 5,153 senior, and 2,343 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (1,863 positions). The remaining 24,200 roles require on-site or hybrid attendance.
The market median for AI roles is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. Highest-paying categories: AI Engineering Manager ($293,500 median, 28 roles); AI Architect ($292,900 median, 108 roles); AI Safety ($274,200 median, 19 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 26,159 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (23,752), AI Software Engineer (598), AI Product Manager (594). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (2,416) are outnumbered by mid-level (16,247) and senior (5,153) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 2,343 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (1,863 positions), with 24,200 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $184,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $309,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $122,200. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Rag (16,749 postings), Aws (8,932 postings), Rust (7,660 postings), Python (3,815 postings), Azure (2,678 postings), Gcp (2,247 postings), Prompt Engineering (1,469 postings), Openai (1,269 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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