ML Platform Engineer – Training Orchestration

Rhoda AI; Mountain View (CA); On-site; USD 100,000 - 140,000; Full time

Job summary

Rhoda AI in Mountain View is seeking a Research Engineer to build and maintain a training platform that powers model development. The successful candidate will develop tooling for experiment management and optimize training processes across large-scale GPU clusters.

This role offers high visibility, direct feedback from researchers, and the chance to build systems that impact model training efficiency significantly. Candidates should have strong software engineering skills and familiarity with advanced training frameworks.

Qualifications

• Strong software engineering skills with experience in MLOps or ML platform engineering.
• Familiarity with distributed training frameworks like PyTorch DDP.
• Experience building experiment tracking and artifact management systems.
• Comfortable managing GPU cluster environments.
• Strong reliability engineering instincts, including monitoring and alerting.

Responsibilities

• Build and maintain training orchestration systems for large-scale distributed model training.
• Develop experiment management tooling for job configuration and tracking.
• Build observability infrastructure for monitoring training runs.
• Optimize and automate the research iteration loop.
• Manage job scheduling for efficient GPU compute utilization.
• Collaborate with teams to support platform needs.

Skills

Software engineering skills

MLOps experience

Distributed training frameworks

Experiment tracking systems

GPU cluster management

Reliability engineering

Tools

Slurm

Kubernetes

AWS

GCP

Azure

Job description

Rhoda AI in Mountain View is seeking a Research Engineer to build and maintain a training platform that powers model development. The successful candidate will develop tooling for experiment management and optimize training processes across large-scale GPU clusters.

This role offers high visibility, direct feedback from researchers, and the chance to build systems that impact model training efficiency significantly. Candidates should have strong software engineering skills and familiarity with advanced training frameworks.