Software Engineer, ML Platform

Benefits

Parental leave

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Non-birth-parent leave

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Family-building benefits

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• Adoption assistance: Not verified
• Surrogacy assistance: Not verified

Mental health support

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Relocation assistance

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Childcare support

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Learning budget

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Salary

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401(k) match

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Market context

Median wage (BLS OEWS)

$116,543 national median

Projected growth (BLS Employment Projections)

+9.8% - Much faster than average

52% above the BLS national median for software engineering aggregate.

Matched to SOC 15-1252 - Software Engineering aggregate by role bucket.

Source: U.S. Bureau of Labor Statistics, OEWS, May 2024 and Employment Projections, 2024-2034.

Schedule

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Weekend work

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Application

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Assessment

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Deadline

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Where they hire

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About this role

Software Engineer, ML Platform Seattle, Washington, United States About Xaira Therapeutics Xaira is an innovative biotech startup focused on leveraging AI to transform drug discovery and development. The company is leading the development of generative AI models to design protein and antibody therapeutics, enabling the creation of medicines against historically hard-to-drug molecular targets. It is also developing foundation models for biology and disease to enable better target elucidation and patient stratification. Collectively, these technologies aim to continually enable the identification of novel therapies and to improve success in drug development. Xaira is headquartered in the San Francisco Bay Area, Seattle, and London. About the Role We are seeking a Software Engineer to join our Platform team to design, build, and deploy the AI infrastructure that powers our world-class research team. In this role, you'll collaborate closely with AI Scientists and other engineers to enable the effective use of thousands of GPUs for training and inferencing cutting-edge biological foundation models. This role spans a range of problems and skillsets, ranging from MLOps of cutting-edge GPU clusters, to backend engineering of control plane APIs. Our ideal candidate has an opinion about slurm or kubernetes for model training, cares about maximizing bandwidth from the storage subsystems to the GPU, and can build the API paved path for submitting training jobs that are able to dispatch to multiple clusters. What You Will Do - Develop and improve our model training system, responsible for dispatching distributed training jobs to clusters across multiple clouds. - Deploy

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