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About This Role
Job Description
Posted Monday, January 26, 2026, 5:00 AM
Who We Are
Magnit is the future of work. Serving hundreds of the world’s most recognizable brands for the past 30+ years, Magnit offers the industry’s first holistic platform for the modern workforce. Magnit's integrated workforce management (IWM) platform supported by data, software, intelligence, and best-in-class services team is key to our clients’ success. It can adapt quickly to regional or industry economic shifts, and provides the speed, scale, flexibility, transparency, and expertise required to meet an organization’s contingent workforce management, talent strategy and broader organization goals. At Magnit, you’ll work with passionate colleagues who collaborate and deliver meaningful results that positively transform the largest companies around the globe.
About the Role
This role is US based, ideally located in EST hours, and will report to the VP, Enterprise Applications.
The Sr. Director of Data, Analytics & AI will lead the execution of the enterprise strategy, governance and of all data, analytics, and artificial intelligence initiatives. This role will be responsible for driving data as a strategic asset, embedding advanced analytics and AI across the business, and enabling decision-making, client experiences, and operational excellence. Reporting to the VP Enterprise Applications, you will work closely with business and technology leaders to ensure data innovation aligns with company growth, retention, and transformation goals.
What You Will Do
Strategy & Leadership
- Define and execute the enterprise data, analytics, and AI vision in alignment with organizational strategy and digital transformation.
- Build and lead a high-performing global data and analytics team spanning data engineering, data science, business intelligence, and AI/ML.
- Establish and oversee enterprise data governance, quality, and compliance (including GDPR, CCPA, HIPAA, and industry regulations as applicable).
Data & Advanced Analytics Platform
- Own the architecture and evolution of the data platform (ingestion, transformation, storage, semantic layer, analytics tooling) to support real-time analytics, self-service, and enterprise reporting.
- Ensure data is reliable, secure, well-governed, and accessible through self-service capabilities for business users.
- Drive standardization and best practices.
Innovation & Enablement
- Drive adoption of advanced analytics, predictive modeling, and AI solutions across the business to enhance customer experience, revenue growth, and operational efficiencies.
- Partner with business units to identify, prioritize, and deliver high-impact use cases leveraging data and AI.
Collaboration & Influence
- Serve as the key data and AI leader to the executive team, Board, and stakeholders.
- Collaborate with CDTO, CISO, Product, Marketing, Finance, and Client Services leaders to ensure data capabilities meet evolving business needs.
- Build strong external partnerships with vendors, startups, and research institutions to keep the company at the forefront of data and AI innovation.
Operational Excellence
- Develop and manage budgets, resource allocation, and vendor relationships for data/AI initiatives.
- Measure and communicate the business value of data and AI investments through KPIs, ROI metrics, and outcomes tied to revenue growth, retention, and efficiency.
- Ensure ethical AI practices, transparency, and risk management in all deployments.
What You Will Need
- 10+ years of progressive leadership experience in data management, analytics, or AI, with at least 5 years in senior leadership roles.
- Bachelor’s degree in Computer Science, Data Science, Engineering, or related field required; Master’s or PhD preferred.
- Proven success in leading enterprise-scale data/analytics transformation and deploying AI/ML solutions in complex global organizations.
- Proven track record of building and scaling modern data and analytics platforms.
- Demonstrated expertise in cloud data platforms, data warehousing, machine learning frameworks, and advanced analytics tools.
- Preferably experience with modern tools Snowflake, DBT, Fivetran, Kafka, Power BI, Sigma and Airflow.
Compensation: Base salary range is $200,000-$225,000 USD annually.
What Magnit will Offer You
At Magnit,you’ll be joining an innovative, high-growth environment and can quickly make an impact to help transform the largest companies in the world. You will work with passionate colleagues who collaborate and deliver. Magnit offers all employees the opportunity for growth and development, and we want individuals to fulfill their potential and blaze their own trails!
Magnit will offer you a competitive benefits package, including unlimited PTO, medical, dental, and vision coverage, retirement planning, as well as discounts and perks for tickets, travel, merchandise and more! Magnit encourages employees to participate in giving back, and we will match employee contributions to favorite charities and support corporate volunteering hours to make a difference in your community!
If this role isn’t for you
Stay in touch, we will let you know when we have new positions on the team. To see a complete list of our open career opportunities please visit
https://magnitglobal.com/us/en/company/careers.html
To do our best work we need different viewpoints. Therefore, we celebrate diversity and embrace inclusion.
As an equal opportunity employer, we are dedicated to building a team that represents a variety of backgrounds, perspectives, and skills. We strive to ensure that we maintain a positive and enriching work environment for all.
By applying to this role, you consent to Magnit safely storing and managing your personal data. Please read this link to learn more.
https://magnitglobal.com/us/en/privacy-notice.html
Job Details
Job Family
Staff Jobs
Pay Type
Salary
Salary Context
This $200K-$225K range is above the median for AI/ML Engineer roles in our dataset (median: $170K across 217 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 37,339 AI roles we're tracking, AI/ML Engineer positions make up 91% of the market. At Magnit Global, 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 $154,000 based on 8,743 positions with disclosed compensation. Director-level AI roles across all categories have a median of $230,600. This role's midpoint ($212K) sits 38% above the category median. Disclosed range: $200K to $225K.
Across all AI roles, the market median is $190,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $300,688. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $85,000; Mid: $147,000; Senior: $225,000; Director: $230,600; VP: $248,357.
Magnit Global AI Hiring
Magnit Global has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $225K - $225K.
Remote Work Context
Remote AI roles pay a median of $160,000 across 1,226 positions. About 7% of all AI roles offer remote work.
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 37,339 open positions tracked in our dataset. By seniority: 3,672 entry-level, 23,272 mid-level, 7,048 senior, and 3,347 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (2,732 positions). The remaining 34,484 roles require on-site or hybrid attendance.
The market median for AI roles is $190,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $300,688. Highest-paying categories: AI Engineering Manager ($293,500 median, 21 roles); AI Safety ($274,200 median, 24 roles); Research Engineer ($260,000 median, 264 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 37,339 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (33,926), AI Software Engineer (823), AI Product Manager (805). 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 (3,672) are outnumbered by mid-level (23,272) and senior (7,048) 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 3,347 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (2,732 positions), with 34,484 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 $190,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $300,688. 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 $145,600. 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 (23,721 postings), Aws (12,486 postings), Rust (10,785 postings), Python (5,564 postings), Azure (3,616 postings), Gcp (3,032 postings), Prompt Engineering (2,112 postings), Kubernetes (1,713 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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