Senior ML Engineer

Accountabilities

• Own machine learning projects end-to-end, from problem exploration and experimentation through production deployment, monitoring, and ongoing iteration.
• Design and build reliable agentic LLM systems using multi-step workflows, tool calling, retrieval, and orchestration techniques suitable for high-stakes clinical environments.
• Develop robust evaluation strategies, including evaluation datasets, offline and online testing harnesses, LLM-as-judge pipelines with human review, and regression testing.
• Improve model performance using evidence-driven approaches such as prompt engineering, retrieval optimization, distillation, fine-tuning, or other appropriate techniques.
• Work across the full machine learning lifecycle, including data preparation, model adaptation, serving, monitoring, and production feedback loops.
• Partner with Product, Clinical, and Engineering stakeholders to translate clinical requirements into technical solutions and identify trade-offs early.
• Review code, share technical knowledge, contribute to engineering best practices, and mentor less experienced engineers.
• Continuously explore and apply AI-assisted approaches that improve individual productivity, team workflows, products, and engineering processes.

Requirements

• Proven experience shipping production machine learning systems that are relied upon by real users or customers.
• Hands-on experience developing and deploying LLM-based systems, including prompting, retrieval, tool calling, and agent-style workflows.
• Strong evaluation expertise, with experience building evaluation datasets, frameworks, testing methodologies, and mechanisms for distinguishing meaningful improvements from statistical or operational noise.
• Strong foundations in machine learning and the ability to select appropriate approaches based on technical requirements, evidence, and trade-offs.
• Experience working with ambiguous or loosely defined problems and transforming them into reliable production solutions.
• Strong software engineering skills, including writing production-quality code, working with distributed systems, and debugging complex machine learning pipelines.
• Clear communication skills and the ability to collaborate effectively with both technical and clinical stakeholders.
• Demonstrated AI fluency, with at least Level 1 proficiency: using AI regularly to improve personal productivity. More senior expectations may include building AI-enabled workflows or embedding AI into products and processes.
• Experience with fine-tuning or preference optimization techniques such as RLHF or DPO is a plus.
• Experience in healthcare AI or other high-stakes domains where system errors can have significant consequences is advantageous.
• Experience building agent frameworks or evaluation tooling from scratch is a plus.
• Open-source contributions, technical writing, or other forms of technical knowledge sharing are valued.
• Willingness to work within a distributed European environment, with the position open to candidates across Europe.