MLOps Engineer
Boltz PBC is an AI-for-science company building biomolecular models, compute infrastructure, APIs, and collaborative tools for molecular design and drug discovery.
Funding history
About Boltz
Boltz develops frontier AI models for biomolecular structure prediction, binding-affinity estimation, and protein and small-molecule design. Its current offerings include open-source models and the commercial Boltz Lab and Boltz API products.
Skills
Candidate Availability
Required and preferred rules are kept separate and reflect the wording in the original posting.
About the Role
You will optimize, deploy, and operate large-scale machine-learning models. You will improve training and inference performance, reduce memory and compute overhead, scale workloads across distributed multi-GPU and cloud environments, and harden production systems for reliable, high-volume use.
Requirements
- 5+ years of industry experience
- Experience deploying and operating machine-learning models in production
- Experience optimizing training and inference workloads
- Experience with distributed frameworks and tooling
- Experience with PyTorch and the scientific Python ecosystem
- Understanding of MLOps practices including experiment tracking, model versioning, reproducibility, and CI/CD
- Software engineering experience building reliable, well-tested, maintainable ML infrastructure
- Ability to collaborate with ML researchers
Responsibilities
- Optimize, deploy, and operate large-scale machine-learning models
- Improve training and inference performance
- Reduce memory and compute overhead
- Scale workloads across distributed multi-GPU and cloud environments
- Profile models to improve throughput and latency
- Harden systems for long-running and high-volume workloads
- Translate research models into production-ready services
Benefits
- Substantial equity ownership