Associate Principal AI & ML Engineer – Evinova

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