Interested in this AI/ML Engineer role at Thermo Fisher Scientific?
Apply Now →About This Role
Work Schedule
Standard (Mon\-Fri)Environmental Conditions
OfficeJob Description
Thermo Fisher Scientific Inc. (NYSE: TMO) is a Fortune 100 global leader in serving science, with annual revenue exceeding $40 billion. Our mission is to enable our customers to make the world healthier, cleaner, and safer. From accelerating life sciences research and solving complex analytical challenges to improving patient diagnostics, therapies, and laboratory efficiency, we support customers across the scientific ecosystem.
With more than 120,000 colleagues worldwide, we deliver an unmatched combination of innovative technologies, purchasing convenience, and pharmaceutical services through our industry\-leading brands. To learn more, visit www.thermofisher.com.
The Job:
The Sr. Manager, Corporate Counsel – AI Compliance will provide strategic legal guidance on the development and use of AI\-enabled products, services, and processes across Thermo Fisher’s global businesses. The role focuses on supporting innovation while managing legal, regulatory, and ethical risks associated with artificial intelligence and automated decision\-making technologies. This position is part of the Global Privacy and Technology Office, collaborating closely with business, digital, and compliance leaders.
Success in this role requires building strong relationships, working effectively across functions and time zones, and influencing through expertise and sound judgment in a highly matrixed organization.
This role can be based at one of the following US Thermo Fisher locations: Hillsboro, Oregon (USA); Raleigh, North Carolina (USA); or Pittsburgh, Pennsylvania (USA).
What will you do?
- Partner with colleagues in Data Privacy, Cybersecurity, IT, IP, HR, Commercial, and Regulatory functions to ensure that product development and business operations align with company policy and global AI requirements.
- Provide practical, business\-oriented legal advice on the use, development, and deployment of AI technologies, including risk assessment, documentation, and compliance with emerging AI regulations.
- Draft, review, and negotiate agreements for AI\-enabled products, services, customer engagements, pilots, and supplier relationships, including terms addressing data use, confidentiality, service commitments, warranties, indemnities, liability, and regulatory responsibility.
- Partner with product, engineering, commercial, privacy, cybersecurity, IP, and regulatory teams to translate AI functionality, services descriptions, and customer commitments into clear and scalable contract terms.
- Maintain and leverage AI\-related templates, clause libraries, playbooks, guidance, and training materials to operationalize AI legal standards across business teams.
- Support multiple Thermo Fisher businesses (AIG, SDG, CCG, LSG, LPG, BPG) in identifying, assessing, and managing AI\-related risks in customer and supplier engagements.
- Advise and collaborate with the Global Privacy and Technology Office to enhance AI governance frameworks, streamline legal service delivery, and share best practices.
- Maintain and organize legally required documentation, ensuring readiness for audits or regulatory reviews.
Who we are looking for:
- Minimum of 4 years’ experience in legal, compliance, privacy, technology, product, or commercial contracting roles, ideally with significant exposure to AI, digital innovation, software, data governance, or technology\-enabled products and services.
- Experience drafting and negotiating commercial, technology, services, software, data, licensing, product, or customer\-facing agreements, including nuanced contract language for emerging products and services.
- Practical understanding of global data protection and emerging AI regulatory frameworks (e.g., EU AI Act, OECD principles, NIST AI RMF).
- Strong judgment, business orientation, and the ability to deliver clear, solution\-focused legal advice in complex or novel situations.
- Collaborative mindset with experience working across multiple time zones and in a matrixed global organization.
- Advanced English proficiency; additional languages a plus.
- Experience in life sciences, healthcare, or technology sectors desirable.
What’s in it for you:
- Shape the future of responsible AI within one of the world’s largest life sciences and technology companies, influencing how artificial intelligence supports scientific innovation and healthcare.
- Work alongside highly experienced legal, privacy, and technology professionals who are defining best practices in digital transformation and ethical AI.
- Join a global organization where your counsel directly supports scientists, engineers, and innovators solving some of the world’s toughest challenges.
- Access outstanding career progression opportunities, mentorship, and exposure to senior leadership in a Fortune 100 environment committed to professional development.
- Benefit from a competitive compensation package, annual incentive bonus, and comprehensive benefits (specifics vary by country).
- Be part of a culture built on Integrity, Intensity, Innovation, and Involvement, where diverse perspectives drive better outcomes and every colleague contributes to Thermo Fisher’s mission.
About us:
Our Mission is to enable our customers to make the world healthier, cleaner and safer. Watch as our colleagues explain 5 reasons to work with us. As one team of 120,000\+ colleagues, we share a common set of values \- Integrity, Intensity, Innovation and Involvement \- working together to accelerate research, solve complex scientific challenges, drive technological innovation and support patients in need. \#StartYourStory at Thermo Fisher Scientific, where diverse experiences, backgrounds and perspectives are valued.
Apply today! http://jobs.thermofisher.com
Thermo Fisher Scientific is an EEO/Affirmative Action Employer and does not discriminate on the basis of race, color, religion, sex, sexual orientation, gender identity, national origin, protected veteran status, disability or any other legally protected status
Compensation and Benefits
The salary range estimated for this position based in North Carolina is $135,800\.00–$203,750\.00\.
This position may also be eligible to receive a variable annual bonus based on company, team, and/or individual performance results in accordance with company policy. We offer a comprehensive Total Rewards package that our U.S. colleagues and their families can count on, which includes:
- A choice of national medical and dental plans, and a national vision plan, including health incentive programs
- Employee assistance and family support programs, including commuter benefits and tuition reimbursement
- At least 120 hours paid time off (PTO), 10 paid holidays annually, paid parental leave (3 weeks for bonding and 8 weeks for caregiver leave), accident and life insurance, and short\- and long\-term disability in accordance with company policy
- Retirement and savings programs, such as our competitive 401(k) U.S. retirement savings plan
- Employees’ Stock Purchase Plan (ESPP) offers eligible colleagues the opportunity to purchase company stock at a discount
For more information on our benefits, please visit: https://jobs.thermofisher.com/global/en/total\-rewards
Salary Context
This $135K-$203K range is below the median for AI/ML Engineer roles in our dataset (median: $180K across 1937 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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Thermo Fisher Scientific, 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 $181,170 based on 12,692 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($169K) sits 6% below the category median. Disclosed range: $135K to $203K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Thermo Fisher Scientific AI Hiring
Thermo Fisher Scientific has 4 open AI roles right now. They're hiring across Research Scientist, AI/ML Engineer. Positions span Lawrenceville, NJ, US, Waltham, MA, US, Pleasanton, CA, US. Compensation range: $110K - $203K.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, 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 Engineering Manager roles lead at $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.