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About This Role
Job Title: AI Enablement and Adoption Lead, Strategy and Operations, Specialty Care GBU
Location: Cambridge, MA or Morristown, NJ (Hybrid \- 3 days a week in office requirement; 20% travel expected)
About the job
The AI Enablement \& Adoption Lead brings AI\-enabled ways of working to the Specialty Care GBU’s senior leadership team and the organizations they run. You will accelerate adoption of the AI systems and platforms already available to the business, build the leadership team’s practical fluency, and change how senior leaders and their teams workday to day — moving AI from something people are interested into something they use by habit.
Reporting to the Global Head of Strategy \& Operations, Specialty Care GBU, this is a senior individual contributor role. It carries the direct sponsorship of the EVP, Specialty Care GBU, giving the work a clear mandate from the top of the business unit. You will partner closely with the Digital, Data, and AI functions that build and govern the platforms the GBU relies on. Success comes through credibility and influence rather than formal authority — earning the confidence of senior leaders and helping them adopt new, AI\-enabled ways of working.
About Sanofi
We’re an R\&D\-driven, AI\-powered biopharma company committed to improving people’s lives and delivering compelling growth. Our deep understanding of the immune system – and innovative pipeline – enables us to invent medicines and vaccines that treat and protect millions of people around the world. Together, we chase the miracles of science to improve people’s lives.
Main responsibilities:
Executive Enablement \& Reverse Mentoring
- Build the AI fluency of the Specialty Care GBU leadership team through hands\-on coaching, reverse mentoring, and working sessions shaped around how each leader actually spends their time.
- Sit alongside senior leaders on real work — strategy reviews, planning cycles, board and ExCo pre\-reads — and show where AI changes the task, rather than demonstrating tools in the abstract.
- Translate what good looks like elsewhere, in other business units and other industries, into practical habits the leadership team can adopt now.
Adoption of AI Platforms \& Ways of Working
- Drive real, sustained adoption of the AI systems and platforms already approved for use across the GBU, closing the gap between what is available and what is actually used.
- Redesign recurring leadership and team workflows — reporting, analysis, meeting preparation, synthesis — around AI, and embed the new approach so it holds after the initial push.
- Identify high\-value use cases across the leadership agenda, prioritize them, and carry them from pilot to routine use.
- Stay current on new and emerging AI tools and features, judge what is genuinely useful for the leadership team, and turn that into practical application.
- Partner with the Digital, Data, and AI functions to bring enterprise capabilities into the GBU, and feed back what leaders need from those platforms.
AI Culture \& Community
- Set the tone for a confident, curious, and responsible AI culture across the leadership team and the teams they lead.
- Build and run an internal network of practitioners and champions so good practice spreads without depending on a single person.
- Create practical, lightweight resources — prompts, playbooks, worked examples — calibrated to senior pharma users rather than generic training.
Responsible \& Compliant Use
- Ensure new ways of working respect the GBU’s regulatory, privacy, data\-protection, and information\-security obligations; partner with Legal, Compliance, Privacy, and the AI governance function to keep adoption inside the lines.
- Build leaders’ judgment about where AI helps, where it must not be used, and how to check its output — particularly around regulated content, confidential information, and personal or patient data.
- Escalate gaps in policy or guardrails rather than working around them.
Measurement \& Impact
- Define what adoption and value mean for this work and track them — usage, time saved, quality, and decisions improved — connecting activity to outcomes leaders care about.
- Report Progress and impact to the Head of Strategy \& Operations and the leadership team.
- Capture what works and reuse it to accelerate adoption across the GBU’s franchises and functions.
About you
Education:
- Bachelor’s degree required; technical, scientific, and business backgrounds preferred.
- An advanced degree or formal AI or data credential is a plus, not a requirement. Demonstrated fluency matters more than pedigree for this role.
Experience:
- 5 years of experience, with a clear track record of putting AI tools and AI\-enabled ways of working into real use inside an organization.
- Hands\-on, current command of today’s AI systems — large language models and assistants such as enterprise tools built on GPT, Claude, Gemini, or Copilot — and a working understanding of agentic and no\-code or low\-code approaches.
- Demonstrated experience changing how people work: driving adoption, running enablement, or leading change, ideally with senior or expert audiences.
- Experience in pharma, healthcare, or another regulated, knowledge\-intensive environment is valued; comfort working within compliance and data\-handling constraints is essential.
- A positive disruptor who has visibly moved a team or function from curiosity to genuine, daily use.
Skills \& Competencies:
- AI fluency: deep, practical, and current rather than theoretical. Can sit down with a leader and make them more effective in the same session.
- Translation: turns technical capability into plain\-language value for non\-technical senior people.
- Adoption focus: cares whether things are actually used and whether they change outcomes, not whether they launched well.
- Responsible\-use judgment: knows where the regulatory and ethical lines sit and works inside them.
- Agility: comfortable with ambiguity and a fast\-moving field, and disciplined about staying current as the tools change.
Leadership skills:
- Influence without authority: earns the trust and attention of senior people and helps them work differently through credibility and usefulness.
- Communication: explains and persuades clearly, adjusting from a skeptical executive to an enthusiastic early adopter.
- Collaboration: works well through the matrix and partners closely with the Digital, Data, and AI functions and across the Strategy \& Operations team.
- Drive and ownership: self\-starting, resilient to the friction that comes with changing established habits, and focused on results.
Languages:
- Fluent in English.
Why Choose Us?
- Bring the miracles of science to life alongside a supportive, future\-focused team.
- Discover endless opportunities to grow your talent and drive your career, whether it’s through a promotion or lateral move, at home or internationally.
- Enjoy a thoughtful, well\-crafted rewards package that recognizes your contribution and amplifies your impact.
- Take good care of yourself and your family, with a wide range of health and wellbeing benefits including high\-quality healthcare, prevention and wellness programs and at least 14 weeks’ gender\-neutral parental leave.
Sanofi Inc. and its U.S. affiliates are Equal Opportunity employers committed to a culturally inclusive workforce. All qualified applicants will receive consideration for employment without regard to race; color; creed; religion; national origin; age; ancestry; nationality; marital, domestic partnership or civil union status; sex, gender, gender identity or expression; affectional or sexual orientation; disability; veteran or military status or liability for military status; domestic violence victim status; atypical cellular or blood trait; genetic information (including the refusal to submit to genetic testing) or any other characteristic protected by law.
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\#LI\-GZ
\#LI\-Onsite
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All compensation will be determined commensurate with demonstrated experience. Employees may be eligible to participate in Company employee benefit programs, and additional benefits information can be found here.
Salary Context
This $161K-$232K range is above the median 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 Sanofi, 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. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($197K) sits 10% below the category median. Disclosed range: $161K to $232K.
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.
Sanofi AI Hiring
Sanofi has 2 open AI roles right now. They're hiring across AI/ML Engineer, AI Product Manager. Based in Morristown, NJ, US. Compensation range: $232K - $257K.
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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