Senior ML Engineer — Biomedical Pipelines

Role overview

A hands-on senior engineering role leading the production side of biomedical machine learning. The engineer partners with researchers to take models out of notebooks and into operating pipelines that are observable, reproducible, and accountable. The position combines production code, data and inference system design, mentoring, and a strong voice in scoping and architecture conversations.

Responsibilities

- Lead the design of end-to-end ML systems covering data ingest, training infrastructure, evaluation, deployment, and monitoring. - Build and maintain reproducible pipelines for biomedical data, with careful versioning of code, data, and models. - Own model serving, observability, and on-call posture for the systems the team operates. - Set and progressively raise quality bars for code, models, and operations. - Mentor mid-level engineers and pair when it accelerates the team. - Contribute to hiring and onboarding.

Requirements

- Five or more years shipping ML systems in production, including time in a senior or tech-lead capacity. - Deep Python and PyTorch or JAX, with strong software-engineering fundamentals. - Experience designing data and feature pipelines at non-trivial scale. - A track record of taking research code into operating production systems. - Comfort with the modern data and MLOps stack, including workflow orchestrators, model registries, container platforms, and GPU scheduling. - Strong written and verbal communication, with comfort engaging senior stakeholders.

Nice to have

- Biomedical, healthcare, or life-sciences domain exposure. - Experience with data-governance constraints around PHI or regulated data, and audit-grade reproducibility. - Distributed training, accelerator scheduling, or large-scale inference experience. - Open-source contributions or published technical writing.

Benefits and work setup

- Genuine technical leadership over real systems and a real team. - A flat, senior-heavy organisation with fewer reporting layers and more direct decisions. - Conference, certification, and learning budget. - Flexible remote work across European and Americas time zones.