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 Senior Applied AI&ML Engineer, you will prototype and build the systems that make those targets real – bringing your scientific and clinical judgement to work with stakeholders to develop ML and agentic systems to drive transparent and actionable recommendations. You’ll integrate multi‑source data - historical trials and scientific datasets into production systems to improve the process of designing clinical trials and increase their probability of success.

Who we are looking for:

We are deliberately looking for someone whose first training was scientific or clinical, not computational. If you know why a particular Phase II endpoint gets chosen, what makes an inclusion criterion unworkable at site level, or how messy real-world data actually is when you try to use it — and you have since started teaching yourself to build with Python, ML and LLMs — you are exactly who we want to hear from.

This is a development role. We will invest in your engineering craft, and you’ll be supported by experienced ML and platform engineers. What we can’t build as quickly is deep domain intuition, so that’s what we’re hiring for. We’d rather have a clinician or scientist who is three projects into their AI journey than an engineer who has never sat in a protocol review.

If you’re early in your career — recently out of a PhD or a doctoral training programme in AI for health, drug discovery or health data science, or making your first move out of clinical practice or academic research — you are welcome here.

What You'll Bring (Essential Requirements):

Foundation

• Domain knowledge - familiarity with drug development, clinical trial design, or real-world data (EHR, claims, prescriptions)
• Ph.D. or equivalent professional experience in a health or life science field (medicine, pharmacy, pharmacology, epidemiology, biostatistics, immunology, neuroscience, translational or clinical research, or bioinformatics).
• Previous industry experience building applied ML/AI systems that have shipped as part of a product and driven measurable business impact.

Machine Learning & AI

• 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 and working with LLMs
• Scientific rigour applied to AI. You ask if what is outputted even makes sense.
• Experience working with scientific datasets/ literature.

Engineering & Delivery

• Python development skills
• Experience with Github
• Awareness of working cloud environments

Communication & Collaboration

• Ability to translate complex technical work into clear narratives for both technical and non-technical stakeholders
• Experience sharing knowledge with peers particularly the scientific/clinical domain.

Nice to Have (Desirable Requirements)

• ML - Experience of classical ML and NLP methods
• Software craft – testing, observability, documentation, code review
• 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, StrandsAgents, LangGraph, CrewAI, or equivalents)
• Real world data sources – HER, claims, prescriptions, registries - and their pitfalls.
• AI-augmented development - effective use of agentic coding assistants (Copilot, Cursor, Claude Code) to accelerate delivery
• Startup-pace experience - comfort with ambiguity, rapid iteration, and wearing multiple hats

Location: St Pancras London (3 days per week onsite / 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.  

We know that regulators, healthcare professionals, and care teams at clinical trial sites do not want a fragmented approach. They do not want a future where every pharmaceutical company provides its own, different digital solutions. They want solutions that work across the sector, simplify their workload, and benefit patients broadly. By bringing our solutions to the wider healthcare community, we can help build more unified approaches to how we all develop and deploy digital technologies, better serving our teams, physicians, and ultimately patients. 

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. This means we’re opening new ways to work, pioneering cutting-edge methods, and bringing unexpected teams together. Interested? Come and join our journey.

Where can I find out more?

Learn more about Evinova: www.evinova.com

Follow Evinova on LinkedIn: https://www.linkedin.com/company/evinova/

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. Join us in our unique and ambitious world.

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