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.

Key responsibilities

• 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 research integrity.
• Collaborate with researchers to translate ideas into maintainable software.

Required profile

• Experience building ML training, inference, or data pipelines in Python.
• Strong Python skills and hands‑on experience with PyTorch, JAX, or comparable frameworks.
• Understanding of machine‑learning experiments, including data, numerical precision, and implementation choices.
• Sound software‑engineering practices such as testing, profiling, version control, and documentation.
• Distributed training or data‑processing experience with tools like 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.

Required skills

• Python
• PyTorch
• JAX
• Hugging Face Transformers
• vLLM
• Weights & Biases
• MLflow
• PyTorch Distributed
• DeepSpeed
• Ray

What we offer

• Competitive compensation and equity.
• Visa sponsorship is available.
• On‑site work in Zürich, Switzerland.