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About This Role
Work Schedule
Standard (Mon-Fri)
Environmental Conditions
Office
Job Description
As part of the Thermo Fisher Scientific team, you’ll discover meaningful work that makes a positive impact on a global scale. Join our colleagues in bringing our Mission to life every single day to enable our customers to make the world healthier, cleaner, and safer. We provide our global teams with the resources needed to achieve individual career goals while helping to take science a step beyond by developing solutions for some of the world’s toughest challenges, like protecting the environment, making sure our food is safe or helping find cures for cancer.
Location/Division-Specific Information:
Pleasanton, CA. 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 screen.
Discover Impactful Work:
We are seeking a Senior Manager, Generative AI Software Engineering, with significant experience in Generative AI and Agentic Computing, as well as a robust background in full-stack development and scalable software architecture.
This role focuses on executing Generative AI initiatives within our Genetic Sciences team. The ideal candidate is a pragmatic, hands-on engineering leader who can manage day-to-day operations, mentor a moderately sized team, and collaborate effectively across functions to deliver high-quality AI-systems and solutions.
You will work with a core team of incredibly talented specialists and leverage the latest Generative AI algorithms and software technologies.
A Day in the Life:
- Gen AI Products & Gen AI Productivity in Product Development within the Genetic Sciences industry:
+ Work closely with senior leaders to translate strategic objectives into actionable engineering tasks.
+ Lead the design, development, and deployment of GenAI-driven applications, including AI-powered assistants and automation agents.
+ Map, build, and train domain-specific foundation model(s) around Thermo Fisher’s Genetic Sciences capabilities.
+ Help build on Prem and in Cloud AI model fine-tuning infrastructure in collaboration with ThermoFisher's core IT teams.
+ Assist in architecting and maintaining scalable, secure, and cost-efficient AI model deployments in Cloud environments.
+ Implement processes to monitor, secure, and optimize data pipelines supporting AI applications.
+ Collaborate with business teams implement product and AI strategies aligned with organizational growth objectives.
+ Participate in KOL engagements with companies and Academic institutions in the Gen AI and health and diagnostics market segment.
+ Articulate use case scenarios and detailed product requirements with specific benchmarks aligned with product goals.
- Technical Leadership & Team Management:
+ Collaborate with, lead, and mentor a team of AI engineers, data scientists, algorithm developers, and software developers to ensure successful project delivery.
Keys to Success:
Education and Experience
- Master's Degree in Computer Science, Data Science, Bioinformatics, Biomedical Engineering, or related field required.
- Deep understanding of domain-specific AI/ML training and inference, Large Language Models (LLMs), Generative AI (Gen AI), and relevant frameworks.
- 8+ years in data science / AI with 2+ years in leadership roles in diagnostics, MedTech, biopharma, or healthcare or allied fields with Biotechnology
- (equivalent combinations of education and relevant work experience may be considered).
- Hands-on expertise with Python/R and core libraries; strong command of statistical modeling, large language models, and data visualization. Strong technical expertise in Gen AI model training and inference, and familiarity working with deep learning frameworks like Pytorch/JAX.
- Proficiency with any of the Gen AI deployment models using OpenAI, Anthropic, or Gemini on platforms like Amazon Bedrock.
- Experience working with relational and NoSQL databases, messaging systems, and modern web/mobile application development practices.
- Practical knowledge of containerization (Docker, Kubernetes) and CI/CD pipeline tools (Jenkins, GitHub Actions).
Knowledge, Skills, Abilities
- Excellent interpersonal skills with the ability to work effectively across diverse teams and coordinate multiple projects in a fast-paced environment.
- Strategic thinker with hands-on execution ability.
- Deep interest in applying technology to solve real-world healthcare and scientific challenges.
Benefits:
We offer competitive remuneration, an annual incentive plan bonus, healthcare, and a range of employee benefits. Thermo Fisher Scientific offers employment with an innovative, forward-thinking organization and outstanding career and development prospects. We offer an exciting company culture that stands for integrity, intensity, involvement, and innovation!
Compensation and Benefits
The salary range estimated for this position based in California is $163,100.00–$244,625.00.
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
Role Details
About This Role
AI Software Engineers build the applications and systems that AI models run inside. They own the API layers, data pipelines, frontend integrations, and infrastructure that turn a model into a product users interact with. Every AI company needs engineers who can build the software around the AI.
The challenge is building reliable systems around inherently unreliable components. Models are probabilistic. They'll give different answers to the same question. They hallucinate. They're slow. They're expensive. Your job is to build an application layer that handles all of this gracefully while delivering a product that users trust and enjoy.
Across the 37,339 AI roles we're tracking, AI Software Engineer positions make up 2% of the market. At Thermo Fisher Scientific, this role fits into their broader AI and engineering organization.
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
What the Work Looks Like
A typical week includes: building API endpoints that serve model inference with caching and fallback logic, designing the data pipeline that feeds context to a RAG system, implementing streaming responses in the frontend, debugging a race condition in the async inference pipeline, and optimizing database queries for the vector search layer. It's full-stack engineering with AI at the center.
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
Skills Required
Full-stack engineering skills with AI integration experience. Python and TypeScript are the most common requirements. You'll need to understand API design, database architecture, and how to build reliable systems around probabilistic outputs. Experience with streaming, async processing, and caching patterns is increasingly important as real-time AI applications proliferate.
Knowledge of vector databases, embedding APIs, and LLM integration patterns (function calling, structured outputs, retry logic) differentiates AI software engineers from general software engineers. Understanding cost optimization (caching strategies, model routing, batched inference) is valuable since inference costs can dominate application economics.
Strong postings describe the product you'll be building, the AI integration patterns you'll work with, and the scale requirements. Look for companies that have existing AI features and need engineers to improve and expand them, not companies that are 'planning to add AI' someday.
Compensation Benchmarks
AI Software Engineer roles pay a median of $252,000 based on 337 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $225,000. This role's midpoint ($203K) sits 19% below the category median. Disclosed range: $163K to $244K.
Across all AI roles, the market median is $190,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $300,688. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $85,000; Mid: $147,000; Senior: $225,000; Director: $230,600; VP: $248,357.
Thermo Fisher Scientific AI Hiring
Thermo Fisher Scientific has 1 open AI role right now. They're hiring across AI Software Engineer. Based in Pleasanton, CA, US. Compensation range: $244K - $244K.
Location Context
Across all AI roles, 7% (2,732 positions) offer remote work, while 34,484 require on-site attendance. Top AI hiring metros: New York (1,633 roles, $204,100 median); Los Angeles (1,356 roles, $179,440 median); San Francisco (1,230 roles, $240,000 median).
Career Path
Common paths into AI Software Engineer roles include Software Engineer, Full-Stack Developer, Backend Engineer.
From here, career progression typically leads toward Staff Engineer, AI Architect, Engineering Manager.
If you're a software engineer, you're already 80% there. Learn the AI integration patterns: RAG, streaming inference, function calling, structured outputs. Build a project that demonstrates you can wrap an AI model in a production-quality application with proper error handling, caching, and user experience. That's the portfolio piece that gets you hired.
What to Expect in Interviews
Technical screens look like standard software engineering interviews with an AI twist. Expect system design questions about building reliable applications around probabilistic models: handling streaming responses, implementing retry logic for API failures, and designing caching strategies for LLM outputs. Coding rounds test standard algorithms plus practical integration patterns like async processing and rate limiting.
When evaluating opportunities: Strong postings describe the product you'll be building, the AI integration patterns you'll work with, and the scale requirements. Look for companies that have existing AI features and need engineers to improve and expand them, not companies that are 'planning to add AI' someday.
AI Hiring Overview
The AI job market has 37,339 open positions tracked in our dataset. By seniority: 3,672 entry-level, 23,272 mid-level, 7,048 senior, and 3,347 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (2,732 positions). The remaining 34,484 roles require on-site or hybrid attendance.
The market median for AI roles is $190,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $300,688. Highest-paying categories: AI Engineering Manager ($293,500 median, 21 roles); AI Safety ($274,200 median, 24 roles); Research Engineer ($260,000 median, 264 roles).
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
The AI Job Market Today
The AI job market spans 37,339 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (33,926), AI Software Engineer (823), AI Product Manager (805). 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 (3,672) are outnumbered by mid-level (23,272) and senior (7,048) 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 3,347 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (2,732 positions), with 34,484 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 $190,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $300,688. 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 $293,500 median, while Prompt Engineer roles sit at $145,600. 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: Rag (23,721 postings), Aws (12,486 postings), Rust (10,785 postings), Python (5,564 postings), Azure (3,616 postings), Gcp (3,032 postings), Prompt Engineering (2,112 postings), Kubernetes (1,713 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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