MLOps Engineer at Boltz

Location: London, England
Type: Full-time
Posted: Jan 6, 2026

Description

Boltz is seeking an experienced MLOps Engineer to optimize, deploy, and operate large-scale machine learning models powering Boltz Lab. The role involves translating trained models into production-ready services, focusing on performance optimization, resource reduction, and workload scaling across distributed systems like multi-GPU and cloud environments. The engineer will collaborate closely with ML researchers to achieve operational excellence.

Required Skills

Machine Learning Models
Distributed Frameworks
PyTorch
Python Ecosystem
MLOps Best Practices

Additional Information

Required Previous Experiences: Strong experience deploying and operating machine learning models in production environments.
Proven ability to optimize training and inference workloads, including profiling performance, reducing memory and compute usage, and improving throughput and latency.
Hands-on experience with distributed frameworks and tooling
Hands-on experience with PyTorch and the scientific Python ecosystem.
Strong understanding of MLOps best practices, including experiment tracking, model versioning, reproducibility, and CI/CD for ML systems.
Strong software engineering fundamentals, with experience building reliable, well-tested, and maintainable ML infrastructure.
Preferred Qualifications: Exposure to computational biology or chemistry workflows and data formats.
Background working with large-scale scientific or numerical workloads.
Experience operating ML systems under real-world constraints such as cost, latency, and reliability.