About the Role
Join an AI research team as a Research Engineer, turning experimental code into production-ready systems. You will build the shared infrastructure and post-training pipelines that help research projects scale while preserving experimental integrity.
What You'll Do
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Turn research prototypes into tested, reusable data generation, training, and evaluation pipelines.
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Build distributed experiment infrastructure for data loading, checkpointing, and collecting agent interactions.
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Profile workloads and improve GPU utilization, memory efficiency, and data throughput.
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Develop tests, experiment tracking, and debugging tools while ensuring changes preserve research results.
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Work with researchers to translate ideas into robust, maintainable software.
What We're Looking For
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Strong Python skills and hands-on experience with PyTorch, JAX, or a comparable machine learning framework.
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Experience building and debugging software for model training, inference, or large-scale data processing.
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Understanding of machine learning experiments and how data, numerical precision, and implementation choices affect results.
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Sound software engineering practices, including testing, profiling, version control, and documentation.
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Distributed training or data processing experience with tools such as PyTorch distributed, DeepSpeed, or Ray is valuable.
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Familiarity with model-serving, machine learning, or experiment tracking tools is a plus; reinforcement learning systems and multimodal datasets are also useful.
Compensation & Benefits
Equity is provided. Visa sponsorship is available.
Location
On-site in Zurich, Switzerland. San Francisco, California is also listed as a work location.
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