Research Engineer, Mid-to-Senior

Join an AI research team to turn experimental code into reliable, production-ready systems. You will build post-training pipelines for generating, processing, and evaluating research data, helping research ideas scale into repeatable experiments.

• Convert research prototypes into tested, reusable data generation, training, and evaluation pipelines.

• Build distributed experiment support for data loading, checkpointing, and collecting 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 experimental results.

• Collaborate with researchers to turn ideas into robust, maintainable software.

• Typically 2 to 10 or more years of relevant experience building machine learning training, inference, or data pipelines in Python.

• Strong Python skills and hands‑on experience with PyTorch, JAX, or a comparable machine learning framework.

• Experience building and debugging software for model training, inference, or data processing at scale.

• 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.

• Experience with distributed tools such as PyTorch distributed, Deep Speed, or Ray is helpful, as is familiarity with Transformers libraries, vLLM, or experiment tracking tools.

• Background in reinforcement learning systems or working with video, audio, or image datasets is a plus.

Competitive compensation with equity is offered, along with health, dental, and vision coverage and professional development support. Visa sponsorship and relocation support are available.

This is a full-time, on‑site role in Zürich, Switzerland.