What are the responsibilities and job description for the Senior ML Infra / MLOps Engineer position at Grafton Sciences?
About Grafton Sciences
We’re building physical general intelligence — autonomous systems that can experiment, reason, and discover in the physical world. With deep technical roots and real-world progress at scale (e.g., a $42M NIH project), we’re pushing the frontier of physical AI. Joining us means inventing from first principles, owning real systems end-to-end, and helping build a capability the world has never had before.
About The Role
We’re seeking a Senior ML Infrastructure / MLOps Engineer to build and operate the infrastructure that powers large-scale training, fine-tuning, RLHF/DPO pipelines, dataset governance, experiment tracking, and model deployment. You’ll design distributed training systems, containerized model runners, data versioning workflows, and reproducible evaluation pipelines that enable rapid iteration across LLMs, RL agents, and surrogate models. This role sits at the heart of the ML stack, ensuring stability, reliability, and performance across all model development.
Responsibilities
Compensation
We offer competitive salary, meaningful equity, and benefits.
We’re building physical general intelligence — autonomous systems that can experiment, reason, and discover in the physical world. With deep technical roots and real-world progress at scale (e.g., a $42M NIH project), we’re pushing the frontier of physical AI. Joining us means inventing from first principles, owning real systems end-to-end, and helping build a capability the world has never had before.
About The Role
We’re seeking a Senior ML Infrastructure / MLOps Engineer to build and operate the infrastructure that powers large-scale training, fine-tuning, RLHF/DPO pipelines, dataset governance, experiment tracking, and model deployment. You’ll design distributed training systems, containerized model runners, data versioning workflows, and reproducible evaluation pipelines that enable rapid iteration across LLMs, RL agents, and surrogate models. This role sits at the heart of the ML stack, ensuring stability, reliability, and performance across all model development.
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.
- 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.
Compensation
We offer competitive salary, meaningful equity, and benefits.
