Research Engineer

About the Role

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.

What You'll Do

• 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.

What We're Looking For

• 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.

Compensation & Benefits

Competitive compensation and equity are offered. Visa sponsorship is available.

Location

On-site in Zürich, Switzerland.