Interested in this AI/ML Engineer role at Regeneron?
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About This Role
Build our future together:
Regeneron’s newly established Enterprise Data \& AI organization is built to drive AI transformation and adoption at scale across every part of the organization. This is a high\-visibility, high\-velocity position where the work is directly tied to business and scientific impact.
As the product leader for Regeneron's AI platforms and tools, the successful candidate will have the potential for massive impact and help shape how the enterprise builds, accesses, and adopts AI capabilities. The Director Product Management – Enterprise AI Platforms \& Tools will set AI platform strategy and roadmap for the platforms and tools that put AI into the hands of colleagues across the organization, from internal AI assistants to secure data\-connected applications to agentic solutions. They will translate business needs into clear technical requirements, frame decisions with a transparent view into their assumptions and the rigor behind them and proactively use data to inspire the next generation of capabilities.
The successful candidate will be part of the Enterprise Data \& AI (ED\&AI) team within the AI Center of Excellence (AICOE), working closely with the Chief AI Officer, the AI Innovation Leader, the Platform Engineering team, Delivery Managers, and BU IT partners. Together with this network, they will leverage Regeneron's rich data and state\-of\-the\-art infrastructure to deliver platforms that are useful, trusted, and adopted at enterprise scale.
When \& where: Tarrytown, NY, 4\+ times a week.
Discover your role:
- Be responsible for the release strategy and roadmap for Regeneron's AI platforms and tools, aligned to the enterprise digital roadmap.
- Build business cases to develop, acquire, or invest in new AI/ML capabilities, datasets, platforms, and tools.
- Prioritize ruthlessly across competing demands, framing recommendations with clear assumptions and analytical rigor.
- Develop deep insight into colleague and end\-user pain points and identify the highest\-value opportunities to improve their experience.
- Lead the technical product development of large\-scale AI platforms, machine learning/AI models, and supporting tooling, partnering with engineering and Business Digital teams to ship high\-quality deliverables.
- Define requirements for core platform capabilities — data access, model serving, search/ranking, automation pipelines, and self\-serve tools.
- Guide the use of cloud technologies (AWS, Azure, GCP) for building and deploying automated ML and analytics pipelines.
- Setting the evaluation framework and criteria, pilot, and deploy new technologies as appropriate, building toward an industry\-leading internal AI ecosystem.
- Drive product adoption: measure usage, gather feedback, and continuously improve.
- Partner with GCC engineering teams to translate platform architecture decisions and product requirements into well\-scoped, production\-ready deliverables; maintain ongoing alignment on roadmap priorities, handoff standards, and delivery quality to ensure continuity between applied AI prototyping and GCC\-led build and operate.
This role requires:
- A Bachelor's degree in related field required, Masters, MBA, or advanced degree preferred
- 12\+ years of progressive experience in technical product management
- Strong working knowledge of large language models (LLMs), foundation models, and modern generative AI — including their capabilities, limitations, and evaluation.
- Familiarity with agentic AI: autonomous and multi\-step agents, tool use, planning/orchestration, and human\-in\-the\-loop design.
- Hands\-on understanding of emerging interoperability standards, including the Model Context Protocol (MCP) for connecting models to tools and data, and Agent\-to\-Agent (A2A) protocols for multi\-agent coordination.
- Experience with retrieval\-augmented generation (RAG), vector databases, embeddings, and grounding models in enterprise data.
- Familiarity with prompt engineering, context engineering, fine\-tuning, and model customization techniques.
- Understanding of AI evaluation and observability — building evals, measuring quality/safety, monitoring drift, and managing model/agent performance in production.
- Awareness of responsible and secure AI practices: guardrails, access controls, data privacy, and AI governance frameworks.
- Familiarity with MLOps/LLMOps tooling and orchestration frameworks for building, deploying, and maintaining AI applications and pipelines.
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)
$183,100\.00 \- $305,200\.00
Salary Context
This $183K-$305K 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 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 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 $218,750 based on 3,817 positions with disclosed compensation. Director-level AI roles across all categories have a median of $272,150. This role's midpoint ($244K) sits 12% above the category median. Disclosed range: $183K to $305K.
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.
Regeneron AI Hiring
Regeneron has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in Tarrytown, NY, US. Compensation range: $245K - $305K.
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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