about 1 month ago
Oakland, CA, USAMid Level / Senior
Base Salary
$180k - $250k/yr
Responsibilities
- Develop infrastructure for large-scale ML training and inference on GPU clusters.
- Implement and maintain security best practices across ML infrastructure.
- Monitor and optimize infrastructure performance, reliability, and cost.
- Evaluate open source infrastructure solutions and cloud providers.
- Build and maintain machine learning pipelines for model inference.
- Implement CI/CD pipelines for machine learning models and services.
- Develop tooling to help researchers transition from experiments to production models.
Requirements
- 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, preferably GCP.
- Experience with containerized workloads and orchestration systems like Kubernetes and Docker.
- Experience building data or ML pipelines.
- Familiarity with CI/CD and infrastructure-as-code practices.
Benefits
- High-growth opportunity with meaningful impact on protein design.
- Competitive compensation package with equity participation.
- 401(k) with a strong employer match.
- Comprehensive benefits including health, dental, and vision insurance.
- Generous PTO policy and commitment to work-life balance.
- Professional development opportunities in AI and biology.