ML Engineer

Role overview

A mid-level engineering role focused on shipping production machine learning systems that hold up under real use: well-grounded, observable, and pragmatic. The engineer works alongside senior engineers and researchers on external engagements and internal products, owning concrete pieces of the pipeline from data ingestion and feature engineering through training, evaluation, and deployment.

Responsibilities

- Design and ship production-grade ML pipelines, from ingest through inference. - Train, evaluate, and iterate on models for forecasting, classification, ranking, retrieval, and decision support. - Own deployment, observability, and on-call rotations for the systems built. - Collaborate with senior engineers, researchers, and clients to translate fuzzy requirements into clear deliverables. - Contribute to internal tooling and the way the team does machine learning.

Requirements

- Two or more years building and shipping ML systems used by real users. - Strong Python, comfortable with PyTorch or TensorFlow, and the modern data stack. - Solid software-engineering fundamentals, including version control, code review, testing, and CI/CD. - Experience deploying models to production via containers, serverless, or managed inference. - Comfort reading papers and translating ideas into working code. - Clear written and spoken English.

Nice to have

- Cybersecurity, biomedical, or fintech domain exposure. - Experience with LLM applications, retrieval, or evaluation. - Familiarity with MLOps tooling such as feature stores, experiment tracking, and model registries.

Benefits and work setup

- Senior peers who care deeply about what they ship. - Real ownership over real systems rather than a narrow slot in a feature factory. - Flexible remote work across European and Americas time zones. - Access to a broader research and security practice.