Software Engineer ML Infrastructure

You will own ML infrastructure for data, model evaluation, training, and validation. You will build evaluation tools and training-data pipelines, connect dataset versions to model performance, support lab-in-the-loop workflows, validate agent-written software, and improve distributed training reliability, throughput, and GPU utilization.

Responsibilities

• Own how virtual cell model performance is measured, beginning with the Model Performance Dashboard backend.
• Design and build self-service evaluation and model-comparison tools for researchers.
• Redesign and operate the pipeline that turns single-cell data into model training sets.
• Use AI agents to find, process, and curate datasets, supported by automated quality checks.
• Own lab-in-the-loop infrastructure that informs laboratory experiments from model-performance results.
• Build or procure validation frameworks for agent-written code and applications.
• Own engineering for reliable, scalable ML training and evaluation.
• Support multi-node training, diagnose distributed-training failures, and improve throughput and GPU utilization.

Requirements

• Bachelor’s degree with 8 years of relevant experience, or a Master’s degree with 6 years of relevant experience.
• Ownership of a production data or ML platform, including pipelines, storage, versioning, and technical direction.
• AI tools and agents
• Evaluation, quality-control gates, or tests for verifying AI-generated output.
• Technical-domain learning and design decision-making.
• Stakeholder requirements gathering.
• Code review, pairing, and technical documentation.
• Python or PyTorch performance debugging using profilers and measurements.
• GPU utilization, memory, and data-loading optimization.
• Work onsite in the Palo Alto office at least 3 days per week.

Benefits

• Annual discretionary bonus eligibility.
• Participation in the Compound Cash Grant Program, with multi-year cash grants paid in semi-annual installments.