Evinova
health-tech
AssociatePrincipalAI&MLEngineer
Neural analysis suggests this role is
optimal for Lead candidates.
“Associate Principal AI & ML Engineer at Evinova. Skills: Machine Learning, Generative AI, Python, Cloud Deployment. Prototype and build systems. Integrate multi-source data”
What You'll Achieve.
Boost clinical trial success by 20%; Cut development time by 3 years; Halve study costs
Industry & Context.
What They're Looking For.
Must Have
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, 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, 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), 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
Familiarity with drug development, clinical trial design, or real-world data (EHR, claims, prescriptions), Experience building secure, compliant ingestion and retrieval systems with provenance tracking, including web automation, parsing, and document processing, Hands-on experience with multi-agent orchestration tools (e.g., Google ADK, LangGraph, CrewAI, or equivalents), Effective use of agentic coding assistants (Copilot, Cursor) to accelerate delivery, Published packages, conference talks, or internal framework development, Comfort with ambiguity, rapid iteration, and wearing multiple hats
What You'll Do.
Prototype and build systems
Integrate multi-source data
Improve clinical trial design
Increase probability of success
How You'll Work.
Team & Collaboration
Translate complex technical work; Share knowledge with peers; Contribute to engineering standards; Contribute to data science standards
Communication Scope
Translate complex technical work into clear narratives
Full Job Description
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
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