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About This Role
Location Gaithersburg, Maryland, United States Job ID R\-256718 Date posted 19/07/2026
WHY JOIN US?Evinova is a health\-tech business focused on accelerating better health outcomes by advancing digital transformation across the life sciences sector. By combining science\-based expertise, evidence\-led rigor, and deep human insight, we design digital solutions that enable healthcare to work better for everyone.
Operating at the intersection of healthcare, technology, data, and analytics, we are helping unlock the full potential of digital health, transforming how clinical research is conducted, how care is delivered, and how patients experience healthcare. Our solutions are built to scale, driving efficiency, improving decision\-making, and ultimately delivering better outcomes for patients worldwide.
At Evinova, we are driven by a shared purpose to transform health through data and digital innovation. Our teams collaborate across disciplines to solve complex challenges, continuously learning and evolving in a fast\-paced, high\-impact environment.
We also recognize the importance of flexibility and balance. Our ways of working support both individual needs and team collaboration. To foster connection and collaboration, employees are expected to work from the office three days per week, creating opportunities for in\-person teamwork, innovation, and meaningful connection.
Introduction to Role:
As Principal AI Engineer, you'll design and implement sophisticated agentic AI systems that power next\-generation life sciences solutions. Working at the intersection of AI research and real\-world healthcare applications, you'll build intelligent agents that can reason, plan, and act autonomously to solve complex clinical challenges.
What makes this role compelling:
- Lead challenging projects in agentic AI, LLM orchestration, and multi\-agent systems
- Build AI agents that directly impact clinical trials, drug discovery, and ultimately patient care
- Collaborate with product teams, clinical experts, and ML engineers in a fast\-paced environment
- Develop automated evaluation systems, prompt optimization techniques, and advanced agent architectures
- Contribute to the AI in life sciences community through publications, conferences, and open\-source work
Example impactful projects include: Intelligent AI agents for clinical document generation, advanced search systems for medical research, clinical trial optimization tools, synthetic patient data generation, and multi\-modal healthcare AI assistants.
Accountabilities:
- Design Advanced AI Systems
+ Build and deploy sophisticated agentic AI solutions using state\-of\-the\-art LLMs
+ Develop novel approaches to agent memory, tool use, and multi\-agent collaboration
+ Build sophisticated NLP systems for retrieval, information extraction, structured generation, graph reasoning.
- Drive Technical Innovation
+ Create automated techniques for agent design, evaluation, and optimization
+ Systematically discover and validate effective prompt engineering approaches for agentic systems
+ Build specialized observability pipelines for continuous model and agent performance monitoring
- Lead Cross\-Functional Collaboration
+ Partner with product, design, and clinical teams to translate AI capabilities into impactful healthcare solutions
+ Mentor engineers and contribute to AI strategy across the organization
- Contribute to the Field
+ Share expertise at conferences and through technical publications
+ Contribute to open\-source projects and help advance best practices in healthcare AI
Essential Skills/Experience:
- Master's Degree in a relevant field (such as mathematics, computer science, data science).
- 4\+ years of industry experience in applied machine learning, with a strong focus on deep learning, NLP, and generative AI.
- Extensive prior experience exploring and testing language model behavior, prompting and building products with language models.
- Expert knowledge of Python and advanced ML/LLM frameworks (e.g., TensorFlow, PyTorch, Google ADK, Crewai, LangChain, LlamaIndex)
- Extensive experience with AWS services (e.g. SageMaker, Bedrock, MSK, EKS, ECS, OpenSearch).
- Deep understanding of agentic AI systems and frameworks (e.g. agentic design patterns, multi\-agent systems, reinforcement learning).
- Excellent communication skills with the ability to articulate complex technical concepts to both technical and non\-technical audiences
Desirable Skills/Experience:
- Ph.D. in a relevant field (such as mathematics, computer science, data science).
- Demonstrated technical leadership experience, including successful delivery of large\-scale AI projects.
- Experience developing complex agentic systems using LLMs.
- Experience with low\-level languages used for implementing high\-performance ML code (C/C\+\+, Rust, CUDA, etc.)
- Contributions to open\-source AI projects or development of proprietary AI frameworks.
- Expertise in areas such as few\-shot learning, meta\-learning, explainable AI.
- Experience with AI ethics, responsible AI practices, and navigating regulatory landscapes for AI deployment in the life science industry.
SO, WHAT’S NEXT?
To be considered for this exciting opportunity, please complete the full application on our website at your earliest convenience – it is the only way that our Recruiter and Hiring Manager can know that you feel well qualified for this opportunity. If you know someone who would be a great fit, please share this posting with them.
Where can I find out more?
- Explore what we’re building: www.evinova.com
- Stay connected and see our impact in action: https://www.linkedin.com/company/evinova/
Apply today to bring smarter, faster clinical trials to life!
Evinova is an equal opportunity employer that is committed to diversity and inclusion and providing a workplace that is free from discrimination. Evinova is committed to accommodating persons with disabilities. Such accommodation is available on request in respect of all aspects of the recruitment, assessment and selection process and may be requested by emailing AZCHumanResources@astrazeneca.com.
\#LI\-Hybrid
*The annual base pay for this position ranges from* *$144,648\.80 \- $189,851\.55* *USD. Base pay offered may vary depending on multiple individualized factors, including market location, job\-related knowledge, skills, and experience.*
*In addition, our positions offer a short\-term incentive bonus opportunity; eligibility to participate in our equity\-based long\-term incentive program. Benefits offered included a qualified retirement program \[401(k) plan]; paid vacation and holidays; paid leaves; and, health benefits including medical, prescription drug, dental, and vision coverage in accordance with the terms and conditions of the applicable plans.*
*Additional details of participation in these benefit plans will be provided if an employee receives an offer of employment. If hired, employee will be in an “at\-will position” and the Company reserves the right to modify base pay (as well as any other discretionary payment or compensation program) at any time, including for reasons related to individual performance, Company or individual department/team performance, and market factors.*
Date Posted
20\-Jul\-2026
Closing Date
06\-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-$189K range is below the median 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 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 $218,750 based on 3,817 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($167K) sits 24% below the category median. Disclosed range: $144K to $189K.
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.
AstraZeneca AI Hiring
AstraZeneca has 3 open AI roles right now. They're hiring across AI/ML Engineer. Based in Gaithersburg, MD, US. Compensation range: $189K - $189K.
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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