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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.
On-site in Zürich, Switzerland.