AI​/ML Engineer

We’re seeking a senior technical leader whose background combines substantial AI/ML research expertise with hands‑on engineering experience. You’ll help shape the architecture and technical direction of our intelligent systems. The remit extends beyond implementation as an individual contributor: you’ll uncover unanswered questions, develop experiments to investigate them, and take ownership of AI development from initial research through production.

Your experience should combine research depth, typically gained through a PhD or equivalent industry research, with substantial work making AI systems dependable on complex, real‑world production data. You’ve created evaluation frameworks, investigated LLM failure modes, and learned to distinguish impressive benchmark results from meaningful evidence of reliability. You bring informed perspectives on measuring AI performance and deploying systems safely in environments where the consequences matter.

AI is central to both our products and our working practices. Across research, engineering, and operations, we make extensive use of AI tools to accelerate execution and support clearer thinking. Everyone is expected to adopt this approach. At a senior level, that means demonstrating how AI can amplify informed judgment without replacing it.

Healthcare offers both extensive data and significant responsibility. We’re looking for someone who understands the importance of both.

• Own the technical strategy for AI/ML across our clinical intelligence products, covering information extraction, summarization, reasoning, and retrieval. Carry that strategy through to implementation.
• Assess weaknesses in existing systems, including inconsistent performance, missing coverage, and evaluation methods that overlook failures encountered in practice.
• Follow developments in LLMs, clinical NLP, retrieval‑augmented generation, RLHF/RLAIF, agents, and structured prediction, bringing relevant advances into production.
• Establish how we evaluate and benchmark models, prioritizing measures relevant to patient care rather than metrics selected simply because they are straightforward to collect.

• Develop and enhance production pipelines for clinical entity extraction, summarization, chart abstraction, and structured inference from unstructured clinical text.
• Shape and refine our use of RAG, prompt engineering, fine‑tuning, and model selection, applying rigorous evaluation throughout.
• Oversee secure AI deployment that complies with HIPAA and SOC 2, focusing on accuracy, auditability, and the risks associated with hallucinations.

• Provide technical guidance on AI decisions, helping the team determine which approaches to explore, diagnose problems, and assess whether models are ready for release.
• Partner directly with clinical subject matter experts to understand how outputs support clinical workflows, where they fall short, and how quality should be defined in practice.
• Mentor engineers and strengthen the team’s understanding of AI/ML best practices, including effective use of AI tools in everyday technical work.

• Substantial expertise in modern AI/ML, LLMs, NLP, information extraction, or a closely related discipline, supported by direct work with leading approaches rather than theoretical familiarity alone.
• A PhD in ML, NLP, computer science, or a related discipline, OR comparable research depth evidenced by publications, substantial open‑source contributions, or impactful AI work in industry.
• Experience creating and owning evaluation frameworks, including defining what to measure, accounting for Goodhart’s Law, and…