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.