ML Platform Engineer

Noah Sinclair

Full-time · Mid-level · New_York

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About the role

ML Platform Engineer needed for a biotech software environment supporting molecular diagnostics workflows. The platform needs stronger standardisation across training environments, artifact management, deployment templates, and reproducibility controls. You will work between data science and platform engineering to reduce friction and create a more reliable path from experimentation to production. Experience in regulated or quality-sensitive environments is helpful.

Responsibilities

Build the standard deployment and experimentation paths used by data science and ML engineering teams
Improve environment reproducibility, artifact management, and model promotion workflows
Create observability and rollback defaults for production model services
Partner with research and product teams to identify the highest-friction platform gaps first
Contribute to documentation that makes the platform understandable to non-platform engineers

Requirements

4+ years of platform, infrastructure, or MLOps engineering
Kubernetes for standardised workload deployment and environment isolation
Docker for reproducible development and serving environments
GCP including managed compute, storage, and identity patterns for technical teams
CI/CD pipeline design for model and service deployment workflows
Python for tooling, templates, and platform automation