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About This Role
Location Cambridge, Massachusetts, United States Job ID R-260943 Date posted 29/09/2026
Modality: Hybrid (3 days in office, 2 days remotely)Location: Cambridge, MA or Gaithersburg, MDWhat You’ll Do
- Design, build, and operate the agentic and LLM-powered systems that biologics scientists rely on, owning them from concept through reliable, monitored production use.
- Build custom agentic skills and tools that encode our scientists’ expertise, so an agent can do real domain work rather than generic chat.
- Connect agents to internal data, models, and services through AZ’s approved integration platforms, in line with AZ data and security standards.
- Build and deploy LLM and agentic applications end to end, including the interfaces and the supporting engineering (authentication, logging, evaluation, error handling) that makes a tool dependable.
- Build the digital pipelines that move data between our models, our high-throughput and automated lab platforms, and the scientists who use the results.
- Partner day to day with protein scientists, computational biologists, and platform engineers, and work in close partnership with BIX, EAI, and R&D IT, reusing shared platforms and data products rather than building in isolation.
- Document your work in GitHub and Confluence so others can maintain and extend it, and meet relevant safety, quality, and compliance standards, including FAIR (Findable, Accessible, Interoperable, Reusable) data practices.
How You’ll Work
You will work in the CLI or the coding environment you prefer, such as VS Code, using code assistants and agents day to day to write and ship code more efficiently. Our stack includes Claude Code, Claude Cowork, GitHub Copilot, M365 Copilot, and HuggingFace, with GitHub and Confluence for code and documentation. You will deploy on AZ infrastructure including scientific computing platforms, AWS, Kubernetes, and Domino, with access to high-end GPU compute (including AZ’s sovereign AI compute platform built on NVIDIA DGX SuperPOD).
What You Bring
Essential Education & Experience
- MS degree in Computer Science, Software Engineering, Computational Biology, Data Science, or a related quantitative field, or equivalent demonstrated ability.
- 3–10 years of relevant software engineering experience.
- Proven, hands-on experience building agentic AI systems and deploying them to production. We look for something you personally built and shipped that people used, whether at a company, a startup, a lab, or a substantial open-source or personal project.
Essential Skills
- Solid Python and/or TypeScript programming skills, and hands-on experience in software development best practices: clean code, version control, testing, and the judgment to build something that keeps working after you have moved on.
- Practical experience building with LLMs and agent frameworks, including tool and function calling, retrieval, and orchestration of multi-step workflows.
- Fluency with modern agentic developer tools such as Claude Code, Copilot, or similar, used daily as part of how you build.
- Clear written and verbal communication, with the ability to explain technical choices to scientists who are not engineers.
- Working knowledge of Unix, SQL databases, REST APIs, and cloud computing (AWS).
Desired Skills
- Experience with tool-integration layers that connect agents to enterprise data, models, and services.
- Experience with AI frameworks such as Pydantic-AI, LangChain, or similar.
- Experience building production retrieval-augmented generation (RAG) pipelines and working with vector databases.
- Experience in at least one compiled programming language (e.g., C, Java, Go, or Rust).
- Experience with the evaluation and guardrails that make LLM applications trustworthy, and cloud deployment practice: containers, Kubernetes, CI/CD, and platforms such as Domino.
- Familiarity with laboratory automation, high-throughput platforms, or scientific data, and awareness of responsible-AI and compliance considerations in a regulated environment. A life-sciences background is not required, but curiosity about biology and drug discovery is.
Who You Are
You learn quickly, care about engineering craft, and communicate clearly with scientists. You will work close to the science and close to your users, alongside teams spanning BE, BIX, EAI, and R&D IT, on work that amplifies our science and helps bring the right medicines to patients faster.
What You’ll Accomplish in Your First Year
First 30 days. Get hands-on with our stack, including our internal AI platforms, agent frameworks, and the custom tools already in use, and ship your first improvement. Sit with the scientists you build for to learn where they lose time. Scope a first use case end to end with your manager.
First 90 days. Deliver production agentic tools that scientific teams use in their day-to-day workflows. Working with BIX and R&D IT, connect an approved internal data source, such as assay data, to our agent tooling through AstraZeneca's (AZ) governed integration platforms. Establish the practices that make agent code dependable: evaluation, guardrails, and human oversight at the right decision points.
First 180 days. Own a small portfolio of shipped tools with measurable adoption and time saved. Help build the digital pipelines that connect our data and models to high-throughput lab platforms. Contribute reusable components to BE's shared skills repository so the next builders inherit your work.The annual base pay for this position ranges from $137,349.40 to $206,091.60 annually. Our positions offer eligibility for various incentives—an opportunity to receive short-term incentive bonuses, equity-based awards for salaried roles and commissions for sales roles. Benefits offered include qualified retirement programs, paid time off (i.e., vacation, holiday, and leaves), as well as health, dental, and vision coverage in accordance with the terms of the applicable plans.
Date Posted
30-sept-2026 Closing Date
14-oct-2026 Our mission is to build an inclusive environment where equal employment opportunities are available to all applicants and employees. In furtherance of that mission, we welcome and consider applications from all qualified candidates, regardless of their protected characteristics. If you have a disability or special need that requires accommodation, please complete the corresponding section in the application form.
Salary Context
This $137K-$206K range is below the median for AI/ML Engineer roles in our dataset (median: $175K across 1356 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 8,943 AI roles we're tracking, AI/ML Engineer positions make up 75% of the market. At AstraZeneca, 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 11,866 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $228,700. This role's midpoint ($171K) sits 19% below the category median. Disclosed range: $137K to $206K.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. 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: $272,250; VP: $235,000.
AstraZeneca AI Hiring
AstraZeneca has 8 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Gaithersburg, MD, US, Cambridge, MA, US, London, England, UK. Compensation range: $108K - $214K.
Location Context
Across all AI roles, 6% (521 positions) offer remote work, while 8,351 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,416 roles, $263,811 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 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. 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 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). 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 (175) are outnumbered by mid-level (4,132) and senior (3,268) 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 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 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 $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. 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 (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 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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