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About This Role
Work Schedule
Standard (Mon-Fri)Environmental Conditions
OfficeJob Description
Today, biotech and biopharmaceutical companies face significant challenges in drug development. At Clinical Research Group (CRG), part of Thermo Fisher Scientific, our Drug Development Digital Solutions are transforming clinical research through purpose-built, CRO-owned technology integrated across the clinical development journey. Our AI-enabled solutions help enable faster study startups, smarter site selection, cleaner data, greater transparency and streamlined regulatory compliance.
We are seeking a Senior Manager, Applied AI to provide technical and delivery leadership for a growing portfolio of artificial intelligence capabilities supporting internal business teams across our organization.
Working at the intersection of AI strategy, product delivery and technical execution, this leader will translate business needs into practical, secure and scalable AI capabilities spanning generative AI, agentic AI, machine learning and intelligent workflows. The role requires strong technical depth and hands-on engagement with AI architecture, engineering, data and platform decisions, while leading and developing a multidisciplinary team and providing end-to-end accountability across multiple AI initiatives.
A key focus will be evolving AI delivery from individual solutions toward a scalable, reusable technical operating model—driving reuse of AI components, services, platforms and architectural patterns; strengthening evaluation, observability and governance practices; improving engineering, development and deployment standards; and ensuring solutions deliver measurable business value, reliability and responsible use.
What you'll do:
- Lead a portfolio of applied AI initiatives from opportunity definition and prioritization through technical design, development, production deployment, adoption and continuous improvement, establishing clear technical roadmaps, delivery plans, measures of success and resource priorities across the portfolio.
- Provide technical and delivery leadership across generative AI, agentic AI, machine learning and intelligent automation, translating business opportunities and workflow challenges into scalable capabilities and making or guiding key decisions across solution architecture, model and platform selection, data and knowledge architecture, orchestration, evaluation and production engineering.
- Lead, coach and develop multidisciplinary technical teams, including AI engineers and data scientists, providing technical direction and mentorship while establishing clear ownership and accountability and allocating resources based on business value, technical complexity, risk, dependencies and capacity.
- Drive reusable enterprise AI capabilities, including platforms, components, services, APIs and architectural patterns, to accelerate delivery, establish consistent engineering practices, reduce future solution costs and prevent unnecessary duplication across teams and technology investments.
- Lead the design and implementation of enterprise-grade AI solutions leveraging large language models, retrieval-augmented generation (RAG), AI agents, orchestration frameworks, machine learning and emerging technologies, with deep engagement across solution architecture, data integration, knowledge and semantic layers, model and platform selection, AI/ML engineering, security considerations and production deployment.
- Establish and continuously improve robust AI evaluation and operational practices, including technical testing, model and application evaluation, monitoring, observability, performance measurement, reliability engineering and continuous improvement to ensure solutions remain accurate, reliable, secure and fit for purpose in production.
- Embed governance and responsible AI practices throughout the AI solution lifecycle, incorporating appropriate data protection, security, privacy, validation and enterprise compliance requirements into architecture, engineering, testing, deployment and ongoing operations.
- Establish and evolve technical standards, reference architectures and development patterns for enterprise AI, helping teams make consistent decisions across model selection, data and knowledge architecture, prompt and agent design, orchestration, evaluation, deployment, monitoring and lifecycle management.
- Partner across Product, Digital, Data, Architecture, Security, Quality, Compliance and business teams, while managing delivery accountability across strategic technology partners, vendors and contingent resources and communicating effectively with executive stakeholders.
- Provide technical oversight across the AI development lifecycle, including solution design, engineering practices, model and application evaluation, testing, deployment, observability, incident response and lifecycle management, ensuring production AI systems meet defined standards for performance, reliability, security and maintainability.
What you need:
- Bachelor's degree with 8-10 years of experience in artificial intelligence, machine learning, data science, software engineering, computer science or a related technology field. Or Advanced degree plus 7 years of relevant experience, as stated above.
- Demonstrated experience leading AI or machine learning teams and complex technology portfolios in an enterprise environment, including multiple concurrent initiatives, technical prioritization, architecture and design decisions, resource allocation and delivery accountability.
- Proven experience taking AI solutions from concept and experimentation through production deployment, adoption and ongoing operation at enterprise scale.
- Strong understanding of generative AI and large language model technologies, including RAG, agentic workflows, tool use, orchestration, grounding, model selection, prompt and context engineering, and production implementation patterns.
- Strong understanding of machine learning, data science and modern AI architecture, with sufficient depth to lead and challenge technical design decisions, evaluate solution quality and provide credible technical direction to engineering and data science teams.
- Experience with major cloud and AI platforms such as Azure, AWS or GCP, as well as modern data, application and AI architectures, with the ability to evaluate technology and platform choices in the context of enterprise-scale AI solutions.
- Working knowledge of Python and modern AI/ML development frameworks, with the ability to engage deeply in technical designs, review implementation approaches, evaluate code and engineering practices, and provide technical direction without serving as the primary hands-on developer.
- Experience designing or operating at scale reusable AI platforms, services, APIs, data pipelines or shared technical capabilities, with demonstrated understanding of AI evaluation, monitoring, model and application performance, data quality, observability and production reliability.
- Experience operating within data governance, security, privacy, regulatory and/or responsible AI frameworks.
- Experience managing external technology partners, consultants or distributed delivery teams, with strong product and business acumen and the ability to connect technology investments to measurable business outcomes.
- Excellent communication and stakeholder-management skills, including the ability to explain complex AI concepts, tradeoffs and risks to technical and executive audiences, lead through ambiguity and adapt as technologies and priorities evolve.
- Life sciences or clinical research industry experience preferred.
At Thermo Fisher Scientific, we are committed to fostering a healthy and harmonious workplace for our employees. We understand the importance of creating an environment that allows individuals to excel. Please see below for the required qualifications for this position, which also includes the possibility of equivalent experience:
- Able to communicate, receive, and understand information and ideas with diverse groups of people in a comprehensible and reasonable manner.
- Able to work upright and stationary for typical working hours.
- Ability to use and learn standard office equipment and technology with proficiency.
- Able to perform successfully under pressure while prioritizing and handling multiple projects or activities.
- May require as-needed travel (0-20%).
Location: Remote USA. Relocation assistance is NOT provided.
- Must be legally authorized to work in the United States without sponsorship.
- Must be able to pass a comprehensive background check, which includes a drug screening.
The annual salary range estimated for this position in North Carolina is $130,000-$180,000 USD. 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.
Salary Context
This $130K-$180K range is below the median for AI/ML Engineer roles in our dataset (median: $176K across 1540 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 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% 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 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 $212,000 based on 11,857 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $228,700. This role's midpoint ($155K) sits 27% below the category median. Disclosed range: $130K to $180K.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
Thermo Fisher Scientific AI Hiring
Thermo Fisher Scientific has 5 open AI roles right now. They're hiring across AI/ML Engineer, AI Product Manager. Positions span Pittsburgh, PA, US, Frederick, MD, NC, US. Compensation range: $110K - $180K.
Location Context
Across all AI roles, 6% (568 positions) offer remote work, while 9,010 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,413 roles, $264,500 median); Los Angeles (1,340 roles, $210,000 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 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 $253,100 median, while Prompt Engineer roles sit at $141,440. 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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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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