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Work Schedule
Standard (Mon\-Fri)Environmental Conditions
OfficeJob Description
At PPD, Thermo Fisher’s clinical research group (CRG), we’re using digital innovation, data science, and AI to reimagine how life\-changing therapies reach patients. Our teams combine deep scientific expertise with advanced analytics, automation, and digital platforms to make research smarter, faster, and more connected.
We know that innovation happens when diverse minds meet. Our Digital Science, Data, and AI professionals collaborate closely with scientists, clinicians, and operational experts to solve real\-world challenges in clinical research. Alongside our partnership with Open AI, you can be part of the collaboration that will help to improve the speed and success of drug development, enabling customers to get medicines to patients faster and more cost effectively.
About the Team:
CRG Digital AI is the engine that translates our digital strategy into scalable, production\-ready AI capabilities that drive measurable business impact. Operating in close partnership with Product, Data, and Engineering, the team embeds AI across our digital portfolio to accelerate clinical trial execution, enhance data\-driven decision\-making, and unlock differentiated value for our customers. Through a combination of centralized platforms, standards, and federated execution, CRG Digital AI enables rapid innovation while ensuring consistency, quality, and responsible AI practices.
About the Position:
Reporting to the VP, Head of Analytics and AI, the Senior Director, AI Platform Architecture is a senior leadership role within CRG Digital responsible for defining, building, and scaling the foundational AI platform that enables the rapid development, deployment, and operation of AI\-enabled products and solutions across CRG. This leader owns the end\-to\-end AI platform strategy, architecture, and delivery model—ensuring that AI capabilities are scalable, reusable, secure, and production\-ready.
Operating at the intersection of Applied AI (AAI), Data Platforms, and Digital Engineering, this role serves as the backbone of CRG’s AI ecosystem—providing the tools, infrastructure, standards, and services required to accelerate AI innovation while maintaining governance, compliance, and operational excellence. The Director will enable both centralized and federated AI execution, empowering product and engineering teams to build AI solutions efficiently and consistently.
Key Responsibilities:
AI Platform Strategy \& Ownership
- Define and execute the AI platform strategy and roadmap, aligned to CRG Digital and AI priorities
- Establish the AI platform as a shared capability layer supporting all AI\-enabled products and workflows
- Ensure alignment with enterprise architecture, data platform (MDP), and security strategies
- Drive a platform\-first approach to AI development, enabling reuse and scalability across domains
Platform Architecture \& Engineering
- Lead the design and development of the AI platform architecture, including:
- Model development, training, and deployment frameworks
- MLOps and LLMOps pipelines
- Model serving, monitoring, and lifecycle management
- Integration with data platforms (e.g., Snowflake, Databricks)
- Ensure platform supports GenAI, agentic workflows, and traditional ML use cases
- Establish standards for performance, scalability, reliability, and cost efficiency
Reusable AI Capabilities \& Tooling
- Build and scale reusable AI components, including:
- Model libraries and templates
- Prompt frameworks and orchestration tools
- Workflow automation and agent frameworks
- Enable rapid development through self\-service tools and developer enablement
- Reduce duplication and accelerate time\-to\-market through standardization and reuse
MLOps, Governance \& Responsible AI Enablement
- Establish and operationalize AI lifecycle management practices, including:
- Model versioning, validation, deployment, and monitoring
- Performance tracking and drift detection
- Partner with AI Risk/Governance teams to embed compliance, security, and responsible AI principles into the platform
- Ensure auditability, traceability, and adherence to regulatory and enterprise standards
Federated AI Enablement
- Provides self\-service platform capabilities to AI Engineering; ensures adoption through ease\-of\-use and standardization
- Enable a federated AI model, allowing domain/product teams to build AI capabilities while leveraging centralized platform standards
- Provide tooling, frameworks, and guardrails to ensure consistency and quality across distributed teams
- Act as a central enablement layer supporting both AAI and product\-aligned engineering teams
Cross\-Functional Integration
- Partner closely with: AAI (Applied AI) for solution design and AI architecture, Data Platforms for data ingestion, quality, and readiness and Digital Engineering for product integration and delivery
- Ensure seamless integration of platform capabilities into AI products and workflows
Partner \& Ecosystem Management
- Define and manage relationships with technology vendors and platform partners (e.g., cloud, AI tooling providers)
- Evaluate and integrate emerging AI technologies and tools into the platform ecosystem
- Optimize the balance between build vs. buy vs. partner decisions
Team Leadership \& Capability Building
- Lead a high\-performing team of AI platform engineers, MLOps specialists, and platform architects
- Define skills, roles, and career paths for AI platform talent
- Drive capability building in AI engineering, platform operations, and emerging AI technologies
- Foster a culture of innovation, reliability, and continuous improvement
Measures of Success:
- Adoption and utilization of the AI platform across CRG Digital teams
- Reduction in time\-to\-deploy AI solutions and increased development velocity
- Increased reuse of AI components and platform capabilities
- Strong performance of AI systems (reliability, scalability, cost efficiency)
- Effective implementation of AI governance and lifecycle management practices
- Development of a scalable and high\-performing AI platform organization
Qualifications:
- Bachelor’s degree required; advanced degree preferred (computer science, engineering, AI/ML, or related field)
- 12 years of experience in software engineering, data platforms, AI/ML engineering, or platform leadership roles
- Proven track record of building and scaling AI/ML platforms or data platforms in enterprise environments
- Strong understanding of AI/ML and GenAI technologies, MLOps/LLMOps practices and Cloud platforms and modern data architectures
- Experience operating in complex, matrixed organizations with cross\-functional stakeholders
- Experience in regulated environments (healthcare/life sciences) preferred
Knowledge, Skills, and Abilities:
- Deep technical and strategic understanding of AI platform architecture and operations
- Strong systems thinking with ability to balance innovation, scalability, and governance
- Ability to translate platform capabilities into business and product impact
- Strong leadership and team\-building capabilities
- Excellent stakeholder management and ability to influence across Product, Data, AI, and Engineering teams
- Ability to operate in a fast\-paced, evolving technology landscape
At Thermo Fisher Scientific, we are committed to fostering a healthy and harmonious workplace for our employees. We understand the importance of creating an environment that allows individuals to excel. Please see below for the required qualifications for this position, which also includes the possibility of equivalent experience:
- Able to communicate, receive, and understand information and ideas with diverse groups of people in a comprehensible and reasonable manner.
- Able to work upright and stationary for typical working hours.
- Ability to use and learn standard office equipment and technology with proficiency.
- Able to perform successfully under pressure while prioritizing and handling multiple projects or activities.
- May require as\-needed travel (0\-20%).
Band 9 level role
Location: Remote US (east coast preference). Relocation assistance is NOT provided.
- Must be legally authorized to work in the United States without sponsorship.
- Must be able to pass a comprehensive background check, which includes a drug screening.
Compensation and Benefits
The salary range estimated for this position based in North Carolina is $167,500\.00–$278,000\.00\.
This position may also be eligible to receive a variable annual bonus based on company, team, and/or individual performance results in accordance with company policy. We offer a comprehensive Total Rewards package that our U.S. colleagues and their families can count on, which includes:
- A choice of national medical and dental plans, and a national vision plan, including health incentive programs
- Employee assistance and family support programs, including commuter benefits and tuition reimbursement
- At least 120 hours paid time off (PTO), 10 paid holidays annually, paid parental leave (3 weeks for bonding and 8 weeks for caregiver leave), accident and life insurance, and short\- and long\-term disability in accordance with company policy
- Retirement and savings programs, such as our competitive 401(k) U.S. retirement savings plan
- Employees’ Stock Purchase Plan (ESPP) offers eligible colleagues the opportunity to purchase company stock at a discount
For more information on our benefits, please visit: https://jobs.thermofisher.com/global/en/total\-rewards
Salary Context
This $167K-$278K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $180K across 1841 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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Thermo Fisher Scientific, 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 in Demand for This Role
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 $218,750 based on 3,817 positions with disclosed compensation. Director-level AI roles across all categories have a median of $272,150. Disclosed range: $167K to $278K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Thermo Fisher Scientific AI Hiring
Thermo Fisher Scientific has 5 open AI roles right now. They're hiring across AI/ML Engineer, AI Software Engineer. Positions span TX, US, VA, US, FL, US. Compensation range: $271K - $335K.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 Safety roles lead at $300,000 median, while Prompt Engineer roles sit at $140,000. 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: Python (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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