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Work Schedule
Standard (Mon\-Fri)Environmental Conditions
OfficeJob Description
At Thermo Fisher’s PPD clinical research group (CRG), we’re using digital innovation, data science, and AI to reimagine how life\-changing therapies reach patients. Our teams combine deep scientific expertise with advanced analytics, automation, and digital platforms to make research smarter, faster, and more connected.
We know that innovation happens when diverse minds meet. Our Digital Science, Data, and AI professionals collaborate closely with scientists, clinicians, and operational experts to solve real\-world challenges in clinical research. Alongside our partnership with Open AI, you can be part of the collaboration that will help to improve the speed and success of drug development, enabling customers to get medicines to patients faster and more cost effectively.
About the Team:
CRG Digital AI is the engine that translates our digital strategy into scalable, production\-ready AI capabilities that drive measurable business impact. Operating in close partnership with Product, Data, and Engineering, the team embeds AI across our digital portfolio to accelerate clinical trial execution, enhance data\-driven decision\-making, and unlock differentiated value for our customers. Through a combination of centralized platforms, standards, and federated execution, CRG Digital AI enables rapid innovation while ensuring consistency, quality, and responsible AI practices.
About the Position:
Reporting to the VP, Head of Analytics and AI, the Senior Director, AI Delivery \& Operations is a senior leadership role responsible for building, operating and scaling CRG Digital’s end\-to\-end AI engineering and platform capability, anchored in reusable architecture and scalable execution systems. This includes ownership of AI engineering delivery, platform architecture, and the integrated automation layer—ensuring that AI\-enabled solutions are production\-ready, scalable, and seamlessly embedded into business operations.
Operating at the intersection of Applied AI (AAI), Data, and Product Engineering, this leader unifies solution delivery and platform enablement into a cohesive, high\-performing system. The role is accountable for both what gets built and how it runs, combining product\-aligned engineering teams with a robust, reusable platform that accelerates development, enforces standards, and enables federated AI adoption across CRG. A core part of this mandate is defining and scaling reusable AI architecture patterns, services, and components that support rapid development of AI\-enabled capabilities across domains.
A central focus of this role is the development of a modern AI\-native execution layer, where AI\-driven decisioning, services, and workflows are operationalized through APIs, orchestration frameworks, and automation capabilities. Traditional RPA is evolved and integrated into this broader architecture as one of several execution mechanisms, rather than a standalone capability, ensuring consistency, scalability, and alignment with AI\-first design principles.
This role plays a critical part in shaping the next generation of role\-based, AI\-enabled operations, where AI capabilities are embedded directly into how work is performed. The platform and engineering organization will define the architectural foundation for these operating models, enabling reusable patterns for human–AI interaction, decision support, and autonomous or semi\-autonomous execution.
The Senior Director, AI Delivery \& Operations partners closely with:
- Solution Architecture to translate AI use cases into scalable technical solutions
- AI Value Realization \& Enablement (AVRE) to ensure solutions are designed for real\-world workflow integration
- AI Risk \& Compliance to embed governance and responsible AI practices into platform and engineering systems
- Data and Product teams to align on priorities, architecture, and delivery outcomes
This role is critical to enabling CRG’s AI strategy by delivering a high\-throughput, platform\-enabled engineering organization that balances speed, quality, cost efficiency, and regulatory compliance—while laying the architectural and engineering foundation for next\-generation AI, agentic systems, and role\-based operating models.
Key Responsibilities
AI Engineering Delivery \& Product Integration
- Lead product\-aligned engineering teams to deliver AI\-enabled applications and services at scale, with a strong emphasis on AI\-native development practices
- Redefine engineering productivity by driving adoption of AI\-assisted and agent\-based development, including AI coding assistants (e.g., Codex\-style tools), agent\-enabled code generation, testing, and refactoring and automated documentation and code review workflows
- Establish a target operating model where individual engineers are significantly amplified by AI tooling, enabling 1 engineer to deliver the output of multiple traditional engineers through effective human–AI collaboration
- Shift engineering focuses on manual coding to solution architecture and system design, validation, testing, and quality assurance of AI\-generated code and integration of AI services into scalable systems
- Own the reliable, high\-quality delivery of AI/ML and GenAI solutions, AI\-enabled product features and APIs and integrated data and feature pipelines
- Establish a high\-throughput engineering model driven by rapid iteration cycles, automation\-first development workflows, reuse of components and services
- Partner with Solution Architecture to translate use cases into scalable, production\-ready solutions, ensuring alignment between design intent and engineering execution
- Ensure seamless integration of AI capabilities into digital products and workflows, with a focus on speed, adaptability, and maintainability
AI\-Native Execution Layer (Automation \& Orchestration)
- Build and scale a modern AI\-native execution layer that operationalizes AI\-driven decisions into real\-world actions
- Integrate and evolve capabilities including APIs and system integrations, workflow orchestration frameworks, intelligent automation (including RPA as a supporting capability)
- Ensure automation is AI\-driven, not task\-driven, reusable and standardized, tightly integrated with platform and AI services
- Enable execution patterns that support human\-in\-the\-loop, semi\-autonomous, and agentic workflows
MLOps, Lifecycle Management \& Operational Excellence
- Establish and scale end\-to\-end AI lifecycle management, including model development, validation, deployment and monitoring and versioning, performance tracking, and drift detection
- Ensure platform and engineering systems meet requirements for reliability and scalability, cost efficiency and observability and monitoring
- Embed governance\-by\-design in partnership with AI Risk \& Compliance, including auditability and traceability and secure and compliant development practices
Partner Strategy \& Capability Scaling
- Define and manage the ecosystem of engineering and platform partners
- Drive effective onshore/offshore and partner delivery models aligned to group needs
- Ensure partners contribute to reusable assets and platform capabilities and speed and quality of delivery
- Lead internal capability building in AI engineering, platform engineering and emerging AI and agentic technologies
Talent \& Organizational Leadership
- Build and lead a high\-performing organization across AI engineering, platform engineering and automation and orchestration capabilities
- Define roles, skill models, and career paths aligned to future\-state AI capabilities
- Foster a culture of engineering excellence, innovation and reuse and accountability and continuous improvement
Measures of Success:
- Adoption and utilization of the AI platform across CRG Digital teams
- Reduction in time\-to\-deploy AI solutions and increased development velocity
- Step\-change improvement in engineering productivity, demonstrated through increased output, reduced cycle times, and effective adoption of AI\-assisted and agent\-enabled development practices
- Successful implementation of AI\-native engineering practices, including widespread adoption of automation\-first development, AI\-assisted coding, and modern DevOps approaches
- Increased reuse of AI components and platform capabilities
- Strong performance of AI systems (reliability, scalability, cost efficiency)
- Effective implementation of AI governance and lifecycle management practices
- Development of a scalable and high\-performing AI platform organization
Qualifications
- Bachelor’s degree required; advanced degree preferred (computer science, engineering, AI/ML, or related field)
- 12 years of experience in software engineering, platform engineering, or technology leadership roles, with a proven track record of building and scaling high\-performing engineering organizations
- Demonstrated experience defining and implementing scalable, reusable platform architectures and shared capability layers in complex enterprise environments
- Experience delivering AI/ML and/or GenAI\-enabled systems in production, including understanding of model lifecycle, integration patterns, and operational considerations
- Proven ability to evolve engineering organizations toward modern, automation\-first and AI\-assisted development practices, driving meaningful improvements in speed, quality, and efficiency
- Demonstrated success driving step\-change improvements in engineering productivity and delivery models, including adoption of AI\-assisted or agent\-based development approaches
- Experience operating in complex, matrixed organizations with cross\-functional stakeholders across Product, Data, AI, and Business teams
- Experience in regulated environments (e.g., healthcare, life sciences) preferred, with an understanding of compliance, security, and quality considerations in engineering systems
Knowledge, Skills, and Abilities
- Strong systems thinking with the ability to design scalable, reusable architecture patterns rather than point solutions
- Deep technical and strategic understanding of AI engineering, platform architecture, and modern software systems, with the ability to translate these into business and operational impact
- Ability to operate effectively at both deep technical and executive levels, bridging architecture, engineering execution, and business priorities
- Strong orientation toward automation, reuse, and platform leverage over bespoke development approaches
- Demonstrated ability to lead transformation of engineering practices, including adoption of AI\-assisted and agent\-enabled development models
- Strong leadership and organizational design capability, with experience building and scaling multidisciplinary engineering and platform teams
- Excellent stakeholder management and communication skills, with the ability to influence across Product, Data, AI, Risk, and Business functions
- Ability to balance speed, quality, cost efficiency, and regulatory compliance in a complex and evolving environment
- Comfortable operating in ambiguity and leading teams through rapidly evolving technology landscapes, including emerging AI and agentic capabilities
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%).
Band 9 level
Location: Remote US (east coast preference). 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.
Compensation and Benefits
The salary range estimated for this position based in North Carolina is $167,500\.00–$278,000\.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 $167K-$278K 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 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 $218,750 based on 3,817 positions with disclosed compensation. Director-level AI roles across all categories have a median of $272,150. Disclosed range: $167K to $278K.
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.
Thermo Fisher Scientific AI Hiring
Thermo Fisher Scientific has 5 open AI roles right now. They're hiring across AI/ML Engineer, AI Software Engineer. Positions span TX, US, VA, US, FL, US. Compensation range: $271K - $335K.
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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