Machine Learning Engineer

MLOps Engineer role | HealthTech company

We’re working with a high-calibre, AI-native healthtech company tackling one of the most complex and manual areas in healthcare - and they’re hiring an MLOps Engineer.

This isn’t a “keep the lights on” infra role. It’s about building the ML backbone from the ground up for systems that directly impact real-world healthcare workflows.

What you’d be doing:

• Designing and scaling end-to-end ML pipelines (training → deployment → monitoring)
• Owning LLMOps infrastructure (evaluation, observability, cost + latency optimisation)
• Building robust, production-grade systems for unstructured healthcare data (docs, claims, clinical text)
• Working closely with applied ML / research teams to take models into real-world use

Tech environment:

Modern stack across cloud, Kubernetes, distributed systems, and LLM tooling. Heavy focus on performance, reliability, and iteration speed.

What they’re looking for:

• Strong background in ML infrastructure / platform engineering
• Experience deploying and scaling ML systems in production
• Exposure to LLMs / GenAI systems (or strong interest in moving into this space)
• Engineers who like ownership, not just implementation

Why it’s interesting:

• Highly technical, first-principles engineering problems
• Real-world impact in healthcare (not just experimentation)
• Small, sharp team — high autonomy and visibility
• Opportunity to shape how ML systems are built from day one

NYC preferred (hybrid), but open to strong candidates for remote working