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About This Role
Location Cambridge, Massachusetts, United States Job ID R\-258047 Date posted 11/08/2026
AstraZeneca are looking for a Senior Forward Deployed Engineer with a strong Chemistry Background based in our new Kendall Square R\&D facilities to continue to build and enhance our next\-generation multi\-modality drug discovery platform.
AstraZeneca has invested heavily in our Augmented Design\-Make\-Test\-Analyze IT (A\-DMTA) toolsets as we seek to deliver better, differentiated candidate drugs into trials, faster, for greater patient benefit. We have made great strides in creating an ecosystem enabling operational and experimental data capture, and in developing scaled analytics, AI/ML\-enabled services, and digital capabilities for both traditional small molecules and complex modalities to drive a Predict First culture in drug discovery. We are extending these capabilities with more advanced modelling, intelligent applications, and platform engineering solutions.
We are looking for a Senior Forward Deployed Engineer with a Chemistry Background to join our team and help build our integrated software platform supporting key drug discovery science.
In this role you will join a global team of engineers, cheminformaticians, architects, business analysts, and product managers in our Augmented Design Make Test Analyse (A\-DMTA) organisation to support small molecule and new modality drug discovery as a Senior Forward Deployed Engineer.
*The following will form part of the role:*
- Partner directly with scientists, product teams, and technical stakeholders to understand high\-value problems and translate them into practical engineering solutions.
- Design, build, deploy, and improve software applications, agentic solutions, MCP servers and tools and services that support key scientific workflows in drug discovery.
- Prototype rapidly, validate with users, and evolve successful solutions into robust, scalable, production\-grade systems.
- Bridge chemistry, cheminformatics, data, and engineering to deliver integrated solutions across scientific and technical workflows.
- Collaborate with product, design, data science, and scientific teams to build high\-impact applications and services.
- Plan, implement, and support platform and infrastructure development with the objective of improving scalability, reliability, performance, and usability.
- Advocate for rigorous engineering practices and discipline, including code reviews, automated testing, logging, monitoring, documentation, and maintainability.
- Help develop and promote a strong software engineering culture across multidisciplinary teams.
- Stay on top of relevant technology trends, experiment with new approaches, participate in internal and external technology communities, and mentor other members of the engineering community.
- Work with modern technology stacks in cloud environments to support scientific software delivery.
Required skills
- Strong software engineering expertise in Python, including advanced object\-oriented design and development of production\-quality applications and services.
- Experience designing, building, and deploying scalable software systems, APIs, and services in production environments.
- Strong problem\-solving and solution design skills, with the ability to translate complex scientific or business needs into practical software solutions.
- Experience working directly with users, stakeholders, or domain experts to gather requirements, refine use cases, and deliver deployed solutions.
- Ability to rapidly prototype solutions, validate them with users, and mature them into robust, maintainable production systems.
- Background in chemistry (PhD preferred, Masters essential), with experience in cheminformatics, chemistry toolkits, chemistry search, and related algorithms.
- Experience integrating systems, services, and data pipelines across complex technical environments.
- Experience with relational databases such as PostgreSQL or Oracle, and/or NoSQL technologies such as Elasticsearch.
- Experience in data analysis, including profiling, investigating, interpreting, and documenting data structures.
- Experience in performance tuning SQL and understanding ETL or data integration pipelines.
- Extensive experience troubleshooting data issues, analysing end\-to\-end data pipelines, and improving application or service performance.
- Good experience in consuming or exposing web APIs.
- Experience designing, developing, and deploying production\-grade scalable applications using container technologies like Docker and Kubernetes.
- Production experience delivering CI/CD pipelines using tools such as GitHub Actions, ArgoCD, or similar.
- Experience creating and evaluating engineering architecture in the cloud.
- Excellent verbal and written communication skills for effective collaboration with engineers, testers, architects, product managers, scientists, and other stakeholders.
- Ability to work effectively in fast\-moving and ambiguous environments, with ownership of delivery from problem definition through deployment and iteration.
The following skills would be advantageous for your application but are not considered essential:
- Experience working with AWS.
- DevOps experience, including deployment workflows and monitoring tools such as Grafana, Prometheus, and GitHub Actions.
- Working knowledge of Scrum or Agile delivery.
- Product ownership or technical leadership experience, with strong stakeholder communication and management.
- Exposure to AI/ML\-enabled applications in production, particularly where these are embedded within broader scientific or engineering platforms.
- Development of agentic systems, MCP servers or tools
- Specification\-driven development
The annual base pay (or hourly rate of compensation) for this position ranges from $144,648\.80 \- 216,973\.20 USD Annual. 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
12\-ago\-2026
Closing Date
10\-sept\-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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