Overview
ABOUT THE ROLE
Full job description
ABOUT THE ROLE 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. WHAT YOU'LL DO
- 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. WHAT WE'RE LOOKING FOR
- 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, DeepSpeed, 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. COMPENSATION & BENEFITS Competitive compensation with equity is offered, along with health, dental, and vision coverage and professional development support. Visa sponsorship and relocation support are available. LOCATION This is a full-time, on-site role in Zürich, Switzerland.
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