Software Engineer, ML Infrastructure (AI)

Location: Palo Alto, California, United States; onsite at least 3 days per week

Base salary: $191,000–$237,000 per year, plus an annual discretionary bonus and eligibility for the Compound Cash Grant Program.

Company

hirify.global is an independent nonprofit research organization combining artificial intelligence and biology to accelerate scientific progress and investigate the causes of complex diseases.

What you will do

• Own the backend and technical direction of the Model Performance Dashboard for Arc’s virtual cell models.
• Design evaluation tools that allow ML researchers to compare models and measure the impact of each dataset version on model performance.
• Redesign single-cell data pipelines for training-set creation, including AI-agent dataset discovery, processing, curation, and automated quality checks.
• Build lab-in-the-loop infrastructure connecting model performance results with experiments planned by scientific leads.
• Develop validation frameworks, tests, evaluation harnesses, QC gates, and policies for safely deploying agent-written code and applications.
• Own train-and-evaluate CI, multi-node training support, distributed-training troubleshooting, throughput, and GPU utilization across Arc’s clusters.

Requirements

• Bachelor’s degree with 8 years of relevant experience, or a Master’s degree with 6 years of relevant experience.
• End-to-end ownership of a production data or ML platform, including pipelines, storage, versioning, and technical direction.
• Experience designing systems with AI tools and agents and making their output verifiable through evaluations, QC gates, or tests.
• Ability to learn unfamiliar technical domains and work directly with stakeholders to define requirements.
• Experience improving Python or PyTorch performance using profiling and measurements of GPU utilization, memory, and data loading.
• Availability to work onsite in the Palo Alto office at least 3 days per week.

Nice to have

• Experience with single-cell or perturbation data, including AnnData or h5ad.
• Experience building model evaluations, benchmarks, evaluation harnesses, or PyTorch/GPU inference systems.
• Experience with multi-node distributed training using DDP, FSDP, NCCL, or SLURM, or improving training throughput.
• Leadership of an initiative with one or two other engineers.

Culture & Benefits

• Work alongside ML researchers, scientists, and experimental biology teams on high-risk, long-term research.
• Long-term philanthropic funding and industry-like technical resources without commercial research pressure.
• Annual discretionary bonus based on individual and institute performance.
• Eligibility for multi-year cash grants through the Compound Cash Grant Program, paid in semi-annual installments.
• Collaboration with Stanford University, UC Berkeley, and UC San Francisco.