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About This Role
Location Cambridge, Massachusetts, United States Job ID R\-258183 Date posted 12/08/2026
### We are seeking an experienced and visionary Associate Principal Scientist to lead Biologics AI innovation at AstraZeneca’s US R\&D centers in Waltham, MA or Gaithersburg, MD. This is a high‑impact scientific leadership role accountable for defining and executing the AI strategy that integrates state‑of‑the‑art machine learning with wet‑lab discovery to accelerate biologics engineering and enable next‑generation biotherapeutics. You will set technical direction, own delivery across multiple programs, and shape data generation at scale—working across computational and experimental functions and with global partners to translate AI into robust, reproducible advances in discovery.
### Key Responsibilities
- ### Strategic leadership and vision: Define and drive the AI strategy for biologics discovery and engineering, setting priorities and roadmaps that integrate AI and wet‑lab capabilities and deliver measurable impact on pipeline goals.
- ### Program ownership: Lead multiple cross‑functional discovery initiatives from problem framing through deployment, ensuring rapid translation of computational insights into experimental design and decision‑making.
- ### Advanced ML innovation: Architect, develop, and guide application of cutting‑edge models—protein language models, structure‑informed and geometric methods, de novo/protein design, and multi‑modal learning that fuses sequence, structure, and biological activity data—to solve high‑value scientific problems.
- ### AI–wet‑lab integration at scale: Establish closed‑loop design–build–test–learn workflows with experimental teams, formalizing feedback cycles, uncertainty quantification, and active learning to improve model reliability and throughput.
- ### Data strategy and governance: Set standards for high‑quality data generation, curation, and metadata; partner with wet‑lab leaders to design assays and campaigns that maximize ML utility and reproducibility; influence data platform evolution in collaboration with informatics and engineering.
- ### End‑to‑end ML lifecycle leadership: Oversee and improve processes across data pipelines, model development, validation, deployment, monitoring, and continuous improvement, including best practices for reproducibility, documentation, and scientific rigor.
- ### Technical mentorship and team development: Mentor and upskill scientists across AI/ML and experimental domains; provide day‑to‑day technical guidance and contribute to recruitment and development of a high‑performing team.
- ### Stakeholder influence and communication: Communicate strategy, progress, risk, and scientific insights to senior stakeholders; influence portfolio decisions and advocate for AI‑enabled approaches internally and with external partners.
- ### External scientific leadership: Drive publications, patents, and external visibility; represent AstraZeneca in collaborations and at scientific venues; evaluate and integrate emerging methods and tools.
### Required Qualifications
- ### Education and experience: PhD in computer science, machine learning, bioinformatics, computational biology, physics, chemistry, mathematics, engineering, or a related quantitative field, with typically 8\+ years of relevant post‑degree experience in academia and/or industry; or a Master’s with 12\+ years of relevant experience.
- ### Domain impact in biologics AI: Demonstrated track record applying AI/ML to proteins, antibodies, or related biologics, with clear examples of methods translated into experimental outcomes, platform capabilities, or pipeline decisions.
- ### Deep technical expertise: Hands‑on leadership in developing and deploying advanced ML (deep learning, generative models, structure‑aware and geometric methods, sequence/structure multi‑modal models) for protein sequence modeling, structure‑informed prediction, de novo design, or biologics optimization.
- ### Closed‑loop integration: Proven success establishing iterative computational–experimental cycles (e.g., active learning, design–build–test–learn), including designing experiments to interrogate model predictions and improve data/model quality.
- ### Lifecycle and systems: Experience leading the full ML lifecycle at scale—data design and preprocessing, model architecture, training/evaluation, deployment, monitoring, and maintenance—using modern ML frameworks (e.g., PyTorch, TensorFlow) and software engineering best practices.
- ### Data and platforms: Experience with cloud‑based ML environments and scalable data workflows; ability to specify requirements and partner with data engineering/IT to evolve production ML systems that support discovery at scale.
- ### Cross‑functional leadership: Strong record of influencing and delivering in matrixed, multidisciplinary environments, bridging AI scientists, computational biologists, protein engineers, and wet‑lab teams across sites.
- ### Scientific communication: Excellent communication skills with the ability to synthesize complex technical concepts for diverse audiences and to shape scientific and portfolio decisions.
- ### Innovation and delivery: Evidence of scientific innovation and impact through publications, patents, platform creation, or deployment of AI methods that materially improved experimental or business outcomes.
### Preferred Qualifications
- ### Protein and antibody engineering: Experience with antibody/nanobody/protein engineering, including de novo design and multi‑objective optimization for developability, stability, and functional performance.
- ### Advanced methodologies: Expertise with generative models (e.g., diffusion, autoregressive LMs), geometric deep learning/graph neural networks, Bayesian optimization, uncertainty quantification, and active learning for guided experimentation.
- ### Multi‑modal learning: Experience integrating heterogeneous data types (sequence, structure, biophysics/biochemistry assays, high‑throughput binding/functional data, bioprocess/developability metrics) into unified models.
- ### Productionization and MLOps: Experience leading deployment of scientific software/ML models into production discovery workflows, including model monitoring, versioning, and compliance with governance standards.
- ### Data generation strategy: Demonstrated ability to design or refine assay strategies and experimental campaigns to maximize downstream ML performance and data reuse, including metadata standards and FAIR principles.
- ### People and project leadership: Prior experience leading scientists and managing complex projects or collaborations; ability to set goals, delegate effectively, and deliver against timelines.
- ### External profile: Strong external scientific presence (peer‑reviewed publications, patents, invited talks, open‑source contributions, or community standards).
### Why Join Us?
### As part of AstraZeneca’s dynamic US biologics R\&D community, you will play a critical role in shaping the future of AI\-driven biologics discovery and engineering. Collaborating across cutting\-edge computational and experimental teams, you’ll drive innovation that brings transformative medicines to patients around the world. You will be supported by a collaborative, inclusive, and empowering environment, with unparalleled opportunities for scientific impact and personal growth.
### *The annual base pay for this position ranges from $144,648\.80 \- $216,973\.20\. 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
13\-Aug\-2026
Closing Date
28\-Aug\-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 $144K-$216K range is above the median for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% 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 $214,900 based on 6,420 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($180K) sits 16% below the category median. Disclosed range: $144K to $216K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
AstraZeneca AI Hiring
AstraZeneca has 5 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Cambridge, MA, US, Gaithersburg, MD, US, Boston, MA, US. Compensation range: $216K - $216K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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