Responsibilities

• Build and maintain scalable infrastructure for training, fine-tuning, RLHF/DPO workflows, and distributed experiments.

• Develop data pipelines, dataset versioning systems, experiment tracking tools, and reproducibility frameworks.

• Operate containerized inference and training environments, CI/CD for models, and evaluation automation.

• Collaborate with LLM researchers, RL scientists, data engineering, and systems teams to support rapid iteration and robust model deployment.

Qualifications

• Strong experience with ML infrastructure, distributed training, experiment management, or production ML systems.

• Familiarity with containerization, orchestration, model runners, dataset governance, and evaluation pipelines.

• Ability to design reliable training and deployment workflows that support high-throughput experimentation.

• Comfortable working across ML, infrastructure, data systems, and engineering teams in a fast-paced research environment.

Above all, we look for candidates who can demonstrate world-class excellence.

Compensation

We offer competitive salary, meaningful equity, and benefits