Associate Principal AI & ML Engineer – Evinova

Associate Principal AI & ML Engineer – Evinova

Full-Time 70000 - 90000 £ / year (est.) Home office (partial)
AstraZeneca GmbH

At a Glance

  • Tasks: Prototype and build AI systems to revolutionise clinical trials and improve patient outcomes.
  • Company: Join Evinova, a pioneering health-tech venture within AstraZeneca.
  • Benefits: Competitive salary, excellent benefits, and flexible working arrangements.
  • Other info: Dynamic team environment with opportunities for rapid career growth and innovation.
  • Why this job: Make a real impact in healthcare by leveraging cutting-edge AI and ML technologies.
  • Qualifications: Ph.D. or equivalent experience in quantitative fields; strong ML and software skills required.

The predicted salary is between 70000 - 90000 £ per year.

It currently takes over 10 years and $1.3B to develop a drug. More than 70% of that investment goes into clinical trials, yet only ~10% of candidates make it from Phase I to approval. Evinova - a new health-tech business within the AstraZeneca Group — is here to change the math. We use advanced algorithms and GenAI to aim high: boosting clinical trial success by 20%, cutting development time by 3 years, and halving study costs.

As Associate Principal AI & ML Engineer, you will prototype and build the systems that make those targets real—blending forecasting, optimization, and evidence synthesis to drive transparent and actionable recommendations. You’ll integrate multi‑source data - historical signals, external context, and real‑world data (RWD) - into production systems to improve the process of designing clinical trials and increase their probability of success.

If you're motivated by meaningful problems and comfortable working outside your existing experience, you'll thrive here. We're looking for generalists with software, data science and ML skills — people who are genuinely curious, committed to continuous learning, and eager to rethink how clinical trials are designed. We value people who build with depth and intention, not just wrap LLM API calls.

What You'll Bring (Essential Requirements):

  • Foundation
    • Ph.D. or equivalent professional experience in a quantitative field (Mathematics, Computer Science, Machine Learning, Statistics, or similar)
    • Previous industry experience building applied ML/AI systems that have shipped as part of a product and driven measurable business impact.
  • Machine Learning & AI
    • Deep experience across classical ML, deep learning, and NLP
    • Hands-on work with generative AI – including prompt engineering, context engineering and multiagent systems and working with managed endpoints (OpenAI, Anthropic, AWS Bedrock) and open-weight models (Hugging Face ecosystem)
    • Knowledge of agentic design patterns - planning, memory, tool use/function calling, RAG - with practical experience in evaluation and guardrails appropriate for regulated environments
  • Engineering & Delivery
    • Strong Python development skills with production sensibilities (testing, observability, documentation)
    • Experience with containers, APIs, and async services (Docker, FastAPI) and CI/CD pipelines (GitHub Actions)
    • Awareness of architectural patterns in deploying applied ML/AI systems in cloud (AWS).
  • Communication & Collaboration
    • Ability to translate complex technical work into clear narratives for both technical and non-technical stakeholders
    • Experience sharing knowledge with peers and contributing to engineering and data science standards and best practices.

Nice to Have (Desirable Requirements):

  • Domain knowledge- familiarity with drug development, clinical trial design, or real-world data (EHR, claims, prescriptions)
  • RAG pipelines at depth- experience building secure, compliant ingestion and retrieval systems with provenance tracking, including web automation, parsing, and document processing
  • Agent frameworks- hands-on experience with multi-agent orchestration tools (e.g., Google ADK, LangGraph, CrewAI, or equivalents)
  • AI-augmented development- effective use of agentic coding assistants (Copilot, Cursor) to accelerate delivery
  • Open source or community contributions- published packages, conference talks, or internal framework development
  • Startup-pace experience- comfort with ambiguity, rapid iteration, and wearing multiple hats

Location: St Pancras London (3 days per week / 60% overall)

Salary: Competitive + Excellent Benefits!

Why Evinova (AstraZeneca)?

Evinova draws on AstraZeneca’s deep experience developing novel therapeutics, informed by insights from thousands of patients and clinical researchers. Together, we can accelerate the delivery of life-changing medicines, improve the design and delivery of clinical trials for better patient experiences and outcomes, and think more holistically about patient care before, during, and after treatment.

Evinova represents a unique opportunity to deliver meaningful outcomes with digital and AI to serve the wider healthcare community and create new standards for the sector. Join us on our journey of building a new kind of health tech business to reset expectations of what a bio-pharmaceutical company can be.

Are you already imagining yourself joining our team? Good, because we can’t wait to hear from you.

When we put unexpected teams in the same room, we unleash bold thinking with the power to inspire life-changing medicines. In-person working gives us the platform we need to connect, work at pace and challenge perceptions. That’s why we work, on average, a minimum of three days per week from the office. But that doesn't mean we’re not flexible. We balance the expectation of being in the office while respecting individual flexibility.

Our mission is to build an inclusive and equitable environment. We want people to feel they belong at AstraZeneca and Alexion, starting with our recruitment process. We welcome and consider applications from all qualified candidates, regardless of characteristics. We offer reasonable adjustments/accommodations to help all candidates to perform at their best.

Associate Principal AI & ML Engineer – Evinova employer: AstraZeneca GmbH

Evinova, a pioneering health-tech venture within the AstraZeneca Group, offers an exceptional work environment where innovation meets purpose. With a strong emphasis on collaboration and continuous learning, employees are empowered to tackle meaningful challenges in drug development using cutting-edge AI and ML technologies. Located in St Pancras, London, Evinova provides a hybrid working model that fosters flexibility while ensuring team connectivity, alongside competitive salaries and excellent benefits that support both personal and professional growth.

AstraZeneca GmbH

Contact Details:

AstraZeneca GmbH Recruitment Team

StudySmarter Expert Advice🤫

We think this is how you could land Associate Principal AI & ML Engineer – Evinova

Tip Number 1

Network like a pro! Reach out to people in the industry, especially those at Evinova or AstraZeneca. A friendly chat can open doors and give you insights that a job description just can't.

Tip Number 2

Show off your skills! If you've got a project or a portfolio that highlights your AI and ML expertise, bring it along to interviews. It’s a great way to demonstrate your hands-on experience and passion for the field.

Tip Number 3

Prepare for the unexpected! Evinova values curiosity and adaptability, so be ready to discuss how you've tackled challenges outside your comfort zone. Share stories that showcase your problem-solving skills.

Tip Number 4

Don’t forget to apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, it shows you’re genuinely interested in being part of our mission.

We think you need these skills to ace Associate Principal AI & ML Engineer – Evinova

Machine Learning
Deep Learning
Natural Language Processing (NLP)
Generative AI
Prompt Engineering
Context Engineering
Python Development

Some tips for your application 🫡

Show Your Passion:When you're writing your application, let your enthusiasm for AI and ML shine through! We want to see how motivated you are to tackle the challenges in drug development and clinical trials. Share your journey and what drives you to be part of this exciting field.

Tailor Your Experience:Make sure to highlight your relevant experience in machine learning and AI systems. We’re looking for folks who can blend their skills with our mission, so don’t hold back on showcasing projects that demonstrate your expertise and impact!

Be Clear and Concise:We appreciate clarity! When describing your skills and experiences, keep it straightforward and avoid jargon overload. Remember, we want to understand your background without getting lost in technical details. Make it easy for us to see why you’re a great fit!

Apply Through Our Website:Don’t forget to submit your application through our website! It’s the best way for us to receive your details and ensures you’re considered for the role. Plus, it gives you a chance to explore more about Evinova and what we stand for!

How to prepare for a job interview at AstraZeneca GmbH

Know Your Stuff

Make sure you brush up on your knowledge of machine learning, AI, and the specific technologies mentioned in the job description. Be ready to discuss your hands-on experience with generative AI and how you've applied it in real-world scenarios.

Show Your Problem-Solving Skills

Evinova is all about tackling meaningful problems. Prepare examples of how you've approached complex challenges in your previous roles, especially those related to clinical trials or data integration. Highlight your ability to think outside the box!

Communicate Clearly

You’ll need to translate complex technical concepts into simple terms for non-technical stakeholders. Practice explaining your past projects in a way that anyone can understand, focusing on the impact and outcomes rather than just the tech jargon.

Be Curious and Open-Minded

Evinova values continuous learning and curiosity. During the interview, express your eagerness to learn new things and adapt to different challenges. Share instances where you've stepped out of your comfort zone to acquire new skills or knowledge.