Research Engineer

Work alongside an AI research team to turn experimental code into production-ready systems. You will build and improve shared post-training infrastructure that supports research data generation, processing, and evaluation.

- Turn research prototypes into tested, reusable data generation, training, and evaluation pipelines. - Build distributed experiment support, including data loading, checkpointing, and collection of agent interactions. - Profile workloads and improve GPU utilization, memory efficiency, and data throughput. - Develop tests, experiment tracking, and debugging tools while preserving the integrity of research results. - Collaborate with researchers to translate ideas into maintainable software.

- Experience building ML training, inference, or data pipelines in Python. - Strong Python skills and hands-on experience with PyTorch, JAX, or comparable machine learning frameworks. - Understanding of machine learning experiments, including how data, numerical precision, and implementation choices affect results. - Sound software engineering practices, including testing, profiling, version control, and documentation. - Distributed training or data processing experience with tools such as PyTorch Distributed, DeepSpeed, or Ray is valuable. - Familiarity with Hugging Face Transformers, vLLM, or experiment tracking platforms such as Weights & Biases or MLflow is useful. - Experience with reinforcement learning systems or multimodal datasets is a plus.

Competitive compensation and equity are offered. Visa sponsorship is available. LOCATION On-site in Zürich, Switzerland.