Interested in this AI/ML Engineer role at Regeneron?
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Build our future together:
Regeneron’s Enterprise Data \& AI organization is newly established and moving fast — built to drive AI transformation and adoption at scale across every part of the enterprise. This is a high\-visibility, high\-velocity position where the work is directly tied to business and scientific impact.
As a key member of our AI Technology, Innovation \& Delivery team, this role leads Regeneron’s enterprise applied AI strategy and innovation agenda, accountable for translating frontier AI capabilities into production\-grade solutions that advance the company’s scientific and business priorities. The Executive Director, Applied AI sets direction across four interconnected domains — Agentic AI Strategy and Platform Evaluation, Enterprise AI Product Management, Emerging Technology and Industry Intelligence, and Forward Deployment Engineering — and is responsible for moving from opportunity to architecture to deployed solutions. The role works in close partnership with the Data \& AI Engineering leader in Regeneron’s Global Capability Center (GCC) in India, where build and operate resides. Together, they ensure that what is designed is architected for platform\-scale delivery and built to last.
When \& where:
- Work Location: Tarrytown, NY
- Hybrid; 4 days per week on site
Discover your role:
- Sets enterprise direction for agentic AI, evaluating and selecting platforms, frameworks, and architecture patterns. Strategy and platform selection reside in this role; build and operate sit within the GCC.
- Leads a team of product managers and product owners accountable for Regeneron’s portfolio of enterprise AI capabilities. Each capability has a defined owner, a roadmap, and adoption metrics — ensuring AI solutions are treated as products, not projects: with clear business sponsors, measurable outcomes, and a path to sustained value.
- Tracks the AI frontier across models, platforms, agentic frameworks, and methods — and translates that intelligence into concrete recommendations tied to Regeneron’s business and scientific priorities.
- Maintains a clear view of how AI is advancing across biopharma and life sciences and identifies where emerging capabilities can solve real problems or unlock new opportunities. Runs structured pilots with clear adoption criteria.
- Leads a team that embeds directly with business and scientific stakeholders to activate AI solutions at speed. Operates in two modes: rapid deployment of configured GenAI and low\-code/no\-code solutions and structured prototype\-to\-handoff engagements for higher\-complexity problems requiring GCC production engineering.
- Set the technical and architectural direction for applied AI at Regeneron. Translate business problems into solution blueprints, architecture patterns, and integration pathways. Own design decisions from concept through prototype with the rigor required for production.
- Lead the agentic AI strategy — defining where Regeneron builds, buys, or partners; and ensuring strategic alignment with GCC for build\-and\-operate execution.
- Lead the Enterprise AI Product Management function — ensuring every enterprise AI capability has a defined roadmap, a business sponsor, and measurable adoption outcomes.
- Lead the Forward Deployment Engineering function — maintaining a clear operational distinction between rapid\-deployment mode and build\-to\-scale mode.
- Connect emerging technology intelligence to specific business problems and scientific opportunities across all departments. Run disciplined pilots with clear adoption criteria.
- Partner with GCC engineering leadership to ensure solutions are buildable, scalable, and maintainable at platform level. Define and govern handoff standards between applied AI prototypes and GCC production engineering.
This role requires:
- Bachelor's degree (Master's or advanced degree preferred) with 17\+ years leading applied AI/ML organizations through the full lifecycle — opportunity identification, architecture, prototype, and production handoff.
- Demonstrated experience building and leading AI product management organizations — establishing product ownership, roadmaps, and adoption accountability for enterprise AI capabilities.
- Strong product mindset paired with hands\-on solution architecture depth; able to operate as both strategist and technical leader — not a pure people\-manager.
- Deep appreciation for the life sciences context — an understanding of how AI is reshaping drug discovery, clinical development, regulatory, and commercial functions in biopharma, and a genuine appetite to engage with Regeneron’s science. Candidates without this orientation will struggle to earn credibility with Regeneron’s scientific and operational leadership.
- Deep familiarity with the current AI landscape: generative AI, agentic systems, LLM application architecture, retrieval and reasoning patterns, and modern AI/ML platforms.
- Hands\-on familiarity with low\-code/no\-code AI deployment and configured GenAI platforms; able to recognize when a business problem calls for rapid configuration versus custom engineering and build a team that executes both modes with equal discipline.
Does this sound like you? Apply now to take your first step towards living the Regeneron Way! We are committed to building a workplace with an inclusive culture. Regeneron is an equal opportunity employer and all qualified applicants will receive consideration for employment without regard to race, color, religion or belief (or lack thereof), sex, sexual orientation, gender identity or expression, gender reassignment, marital or civil partnership status, civil status, pregnancy or parental status, age, disability, nationality, citizenship status, ethnic or national origin, membership of the Traveler community, familial status, genetic information, military or veteran status, or any other characteristic protected under applicable law. Where required, we will provide reasonable accommodation to applicants with known disabilities or chronic illnesses during the recruitment process, unless such accommodation would impose undue hardship.
Where necessary, we disclose salary ranges for roles in all countries in which we operate. The final offer will be determined within the relevant range based on the country of employment, specific role level, and your skills and experience. In some countries, collective bargaining agreements (CBAs) may apply and influence certain elements of pay or benefits. Regeneron offers a competitive and comprehensive total rewards package which may include, depending on country and role: annual bonuses or other incentive plans, equity awards, pension or retirement benefits, 401(k) company match, health and wellness programs, fitness centers, insurance benefits (e.g. medical, dental, vision, life and disability), paid time off, and family support benefits. For additional information about Regeneron benefits in the U.S., please visit https://careers.regeneron.com/en/working\-at\-regeneron/total\-rewards/. For other locations, additional information will be provided during the recruitment process. If you have any questions, please speak with your recruiter.
Please be advised that at Regeneron, we believe we do our best work when we are together. For that reason, many roles are required to be performed on‑site. Please speak with your recruiter and hiring manager for more information about on‑site expectations for your role and location.
As part of the recruitment process, certain background checks may be conducted in accordance with the laws of the country where the position is based. The purpose of such checks is to verify certain information prior to the commencement of employment such as identity, right to work and educational qualifications.
For jobs in Canada: this posting is for an existing position.
Salary Range (annually)
$285,600\.00 \- $475,900\.00
Salary Context
This $285K-$475K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $180K across 951 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 1,809 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Regeneron, 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 $185,000 based on 13,200 positions with disclosed compensation. Director-level AI roles across all categories have a median of $250,000. This role's midpoint ($380K) sits 106% above the category median. Disclosed range: $285K to $475K.
Across all AI roles, the market median is $200,700. Top-quartile compensation starts at $254,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Safety ($274,200) and AI Engineering Manager ($268,700). By seniority level: Entry: $97,760; Mid: $165,778; Senior: $227,400; Director: $250,000; VP: $250,000.
Regeneron AI Hiring
Regeneron has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Tarrytown, NY, US. Compensation range: $475K - $475K.
Location Context
Across all AI roles, 16% (294 positions) offer remote work, while 1,505 require on-site attendance. Top AI hiring metros: New York (2,760 roles, $211,000 median); San Francisco (2,258 roles, $253,000 median); Los Angeles (1,841 roles, $195,000 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 1,809 open positions tracked in our dataset. By seniority: 34 entry-level, 797 mid-level, 728 senior, and 250 leadership roles (Director, VP, C-Level). Remote roles make up 16% of the market (294 positions). The remaining 1,505 roles require on-site or hybrid attendance.
The market median for AI roles is $200,700. Top-quartile compensation starts at $254,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Safety ($274,200 median, 57 roles); AI Engineering Manager ($268,700 median, 42 roles); Research Engineer ($260,000 median, 442 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 1,809 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (1,274), Data Scientist (145), AI Software Engineer (132). 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 (34) are outnumbered by mid-level (797) and senior (728) 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 250 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 16% of all AI roles (294 positions), with 1,505 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 $200,700. Top-quartile roles start at $254,000, and the 90th percentile reaches $307,500. 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 $274,200 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 (877 postings), Aws (592 postings), Azure (458 postings), Rag (380 postings), Gcp (364 postings), Pytorch (277 postings), Prompt Engineering (266 postings), Claude (250 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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