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About This Role
### Build our future together:
At Regeneron, we use science and innovation to develop life\-changing medicines for people with serious diseases. We're looking for a Distinguished Applied AI Scientist to join our new Enterprise Data \& AI team. You'll act as the technical anchor connecting our scientific AI/ML engineering work with frontier technology evaluation. You'll bring deep expertise in foundation models and generative AI, and partner closely with Research and Preclinical Development scientists to deliver lasting impact. You'll help shape platform strategy, model evaluation standards, and how we adopt new AI capabilities. This role reports to the SVP, Chief AI Officer, and offers a chance to help define how a fast\-growing, science\-driven company builds and governs AI at scale.
### When \& where:
- Location: Tarrytown New York, Warren New Jersy, or Armonk New York
- Work model/flexibility: 4 Days a week on\-site
### Discover your role:
- Partner with Research and Preclinical Development scientists to turn their needs into working AI/ML solutions
- Guide an embedded AI/ML engineering team on model development, evaluation, and deployment
- Design foundation model and generative AI solutions for drug discovery and computational biology
- Evaluate new AI models, tools, and platforms against Regeneron's scientific and business needs
- Build proof\-of\-concept projects to test promising technologies before wider rollout
- Track advances in AI research and turn them into clear, practical recommendations
- Support and grow the skills of engineers and applied scientists on the team
- Share findings and recommendations with technical and executive stakeholders alike
This role might be for you if you:
- Have 15\+ years of experience in AI/ML research and engineering, with strength in foundation models and generative AI
- Enjoy turning complex research into guidance that platform, engineering, and product teams can use
- Have partnered with scientists or other domain experts to build real, production\-grade AI/ML solutions
- Have compared and benchmarked new AI models or platforms against real\-world use cases
- Communicate clearly with both technical teams and executive audiences
- Like connecting research breakthroughs to practical, everyday adoption
- Work well across teams that span research, engineering, and business
### This role requires:
- An advanced degree in computer science, engineering, physics, or a related field, plus 15\+ years of relevant experience
- Strong knowledge of deep learning architectures (CNNs, RNNs, GNNs, Transformers) and generative modeling methods (VAE, diffusion, flow\-based, autoregressive)
- Hands\-on experience with foundation model pre\-training and scaling methods (FlashAttention, LoRA)
- Comfort with DL infrastructure and HPC frameworks (Lightning, Ray, FSDP, DeepSpeed)
- Proficiency in Python, with familiarity in common ML frameworks (PyTorch, scikit\-learn)
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)
$242,000\.00 \- $403,300\.00
Salary Context
This $242K-$403K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 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 $214,900 based on 6,420 positions with disclosed compensation. Director-level AI roles across all categories have a median of $274,554. This role's midpoint ($322K) sits 50% above the category median. Disclosed range: $242K to $403K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Regeneron AI Hiring
Regeneron has 4 open AI roles right now. They're hiring across AI/ML Engineer, AI Safety. Based in Tarrytown, NY, US. Compensation range: $360K - $424K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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