ML Platform / MLOps Engineer

As we continue to push the boundaries of what is possible, we’re seeking an ML Platform / MLOps Engineer on the machine learning team to build and operate the infrastructure that powers our machine learning systems. You will work closely with machine learning scientists, protein design scientists, and engineers to enable reliable, scalable platforms for training, evaluating, and deploying large-scale generative biology models.

As an early member of the company, you’ll have significant ownership over the systems and tools that enable our research team to move quickly from experiments to production models.

• Infrastructure supporting large-scale generative models for proteins
• Systems that process massive biological datasets
• Experimentation platforms that enable rapid iteration by ML researchers
• Production services powered by machine learning models

• →Develop infrastructure that enables researchers to run large-scale ML training and inference workloads reliably and efficiently on GPU clusters
• →Implement and maintain security best practices across our ML infrastructure, including access control, secrets management, and least-privilege policies
• →Monitor and optimize infrastructure performance, reliability, and cost
• →Evaluate different open source infrastructure solutions and cloud providers
• →Build and maintain machine learning pipelines to support model inference workloads
• →Implement CI/CD pipelines for machine learning models and services
• →Develop tooling that helps researchers move quickly from experiments to production models

• BS in Computer Science or a related field
• 3+ years of experience building or operating production ML systems
• Experience with MLOps, ML infrastructure, or ML platform engineering
• Strong experience with cloud infrastructure (GCP preferred)
• Experience working with containerized workloads and orchestration systems (Kubernetes, Docker)
• Experience building data or ML pipelines
• Familiarity with CI/CD and infrastructure-as-code practices

• Experienced with the challenges of working with large scale ML models
• Experience with transitioning research ideas into production
• Familiarity with ML frameworks such as PyTorch, MLFlow
• Interested in the intersection between biology and AI

Applicants must have ongoing work authorization in the United States that does not require employer sponsorship. Sponsorship will not be provided now or at any time in the future for this position.

Legal authorization to work in the United States is required. In compliance with federal law, all persons hired must verify their identity and work eligibility and complete the required employment verification form upon hire.