Member of Technical Staff - ML Platform Engineer at Preference Model · LumiJobs

Responsibilities

An RL environment is only useful once it runs reliably inside a lab's training stack. Every environment we build has to be packaged, sandboxed, run at scale, checked against each lab's requirements, and delivered in the format that lab's platform expects. Much of that work is still manual today, and it takes time away from the people designing environments.
As an MTS on our ML Platform team, you will own the systems that take an environment from a builder's repo to a lab's training run. That includes Karotte, our open-source framework for defining RL environments, the internal platform that builds, runs, and reviews environments on CPU, GPU, and TPU, and the integrations that deliver them to each lab we work with. This is an engineering role where you will ensure what our Capabilities team builds runs and ships cleanly.
You'll join a small Platform team and work directly with environment builders and post-training researchers. In your first six months, success looks like a delivery path to each partner lab that is mostly automated: tasks marked ready get built, validated against that lab's requirements, and packaged without hand work. Our needs change quickly, so you'll also pick up other platform and infrastructure work as priorities shift.
Own the integrations that deliver our environments to partner labs: build container images, match each lab's submission format, and automate the checks that confirm requirements are met before anything ships. Do the delivery work by hand where you have to, then automate it.
Build and maintain Karotte, our open-source environment framework, from API design through releases and migrations that keep existing environments working.
Run and scale the infrastructure that builds, runs, and grades environments, including containerized rollouts and grading across CPU, GPU, and TPU, VM-based sandboxing, and the resource limits and isolation that stop a model from gaming its own grader.
Build the internal tools environment builders use every day (CLI, docs, data catalog, access controls, and run monitoring) so they spend their time on environment design instead of infrastructure.
Partner with environment builders and post-training researchers to turn recurring pain points into platform features, and ship quickly in response to their feedback.
What We are Looking For
Strong software engineering fundamentals and a track record of building and operating production infrastructure or developer platforms that other engineers depend on.
Proficiency in Python, including writing libraries and APIs that other people build on.
Hands-on experience with containers and orchestration (Docker, Kubernetes, or similar) and at least one major cloud platform (GCP, AWS, or similar).

Member of Technical Staff - ML Platform Engineer

Responsibilities

Qualification

Preferred

Benefits

Company

Preference Model

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