Principal Machine Learning Engineer (Life Science AI)

TL;DR

Principal Machine Learning Engineer (Life Science AI): Designing and scaling ML infrastructure for generative models and reasoning frameworks to power automated scientific discovery in medicine with an accent on distributed training and production scalability. Focus on building large-scale GPU pipelines, integrating experimental feedback loops, and bridging the gap between research prototypes and production systems.

Location: San Francisco, CA USA

Salary: $252,000 - $374,000 USD

Company

hirify.global is building Scientific Superintelligence to automate the scientific method across medicine, materials, and energy using advanced AI models and proprietary instruments.

What you will do

• Design and optimize large-scale distributed training pipelines for generative models on biological and chemical data across GPU clusters.
• Own production ML systems end-to-end, including deployment, serving infrastructure, monitoring, and reliability.
• Architect infrastructure supporting sequence design, structure prediction, and multimodal scientific reasoning workloads.
• Drive the "Lab-in-the-Loop" lifecycle by integrating experimental feedback loops into pipeline models.
• Define and advance ML engineering standards, tooling, and best practices across the AI organization.
• Collaborate with AI scientists to translate research prototypes into robust, scalable production systems.

Requirements

• Master's or PhD in Computer Science, Machine Learning, or a related quantitative field (or Bachelor's with equivalent experience).
• 10+ years of hands-on experience building and operating production ML systems at scale.
• Deep expertise in distributed training infrastructure and large-scale GPU clusters (AWS, GCP, or on-prem).
• Strong software engineering fundamentals in system design, production-grade code, CI/CD, and observability.
• Proficiency in ML frameworks such as PyTorch, JAX, or TensorFlow.
• Must be based in San Francisco, CA USA.

Nice to have

• Experience with generative models applied to biological sequences, molecular structures, or scientific data.
• Experience with agentic frameworks, active learning loops, or closed-loop experimental workflows.
• Contributions to open-source ML tools or infrastructure projects.
• Familiarity with molecular biology, genomics, protein engineering, or nucleic acid design.

Culture & Benefits

• Competitive base compensation with bonus potential and generous early-stage equity.
• Comprehensive U.S. and International benefits packages.
• High-velocity startup environment focusing on solving historic scientific challenges.
• Culture guided by values of truth, trust, curiosity, grit, and velocity.