Machine Learning Engineer

Role overview Build and operate the production systems that support machine-learning models, from the data pipelines that feed them to training, serving and monitoring. The role connects research with engineering, turning model ideas into reliable, high-throughput systems and working closely with researchers throughout delivery.

Responsibilities - Train and fine-tune models, then deploy them into production. - Build and maintain model-serving and inference systems, including MLOps capabilities at scale. - Develop data pipelines and feature stores that provide dependable inputs for models. - Support the machine-learning lifecycle from data preparation and training to deployment and monitoring. - Improve distributed systems and system performance for large, real-time workloads. - Collaborate with researchers to ship and operate models.

Requirements - Master's degree in computer science with coursework in machine learning. - At least five years of experience in production environments. - Strong software engineering skills and production-grade Python. - Experience training or fine-tuning models and deploying them to production. - Practical knowledge of model serving, inference, MLOps and data engineering. - Experience with distributed systems, performance engineering and large-scale, real-time data, plus a focus on reliability and quality.

Nice to have - Experience with C++, Go or JAX.

Benefits and work setup - Full-time, remote role.