ML Engineer: Scalable GenAI for Biology

the Homebase; Greater London; Hybrid; GBP 60,000 - GBP 80,000; Full time

Job summary

A leading lab in synthetic biology is seeking a machine learning research engineer to manage high performance computing and model serving initiatives. The ideal candidate has extensive experience with Kubernetes, major cloud platforms, and production engineering. Responsibilities include deploying and optimizing compute clusters and designing ML solutions. This position offers competitive compensation, health insurance, generous leave policies, and a hybrid working environment, fostering innovation in synthetic biology.

Benefits

Private health insurance

Pension/401(K) contributions

Generous leave policies

Hybrid working

Travel opportunities

Qualifications

• Extensive experience in machine learning and model deployment.
• Strong background in managing high-performance computing environments.
• Ability to develop dynamic compute solutions.

Responsibilities

• Deploy, maintain, and optimize production and research compute clusters.
• Design and implement scalable ML inference solutions.
• Contribute to productizing model APIs for external use.

Skills

Deep experience with Kubernetes and containerized workflows

Experience with major cloud platforms (AWS, GCP, Azure)

Knowledge of DevOps and related tools (Terraform, etc)

Knowledge of HPC frameworks (Slurm, Ray, etc)

Production engineering & reliability experience

PyTorch & distributed computing experience

Job description

A leading lab in synthetic biology is seeking a machine learning research engineer to manage high performance computing and model serving initiatives. The ideal candidate has extensive experience with Kubernetes, major cloud platforms, and production engineering. Responsibilities include deploying and optimizing compute clusters and designing ML solutions. This position offers competitive compensation, health insurance, generous leave policies, and a hybrid working environment, fostering innovation in synthetic biology.