Member of Technical Staff - ML Platform Engineer

Preference Model is a superintelligence data research company building reinforcement learning environments for training capable, better-aligned AI systems. The ML Platform Engineer will build and operate the systems, frameworks, infrastructure, and integrations that package, validate, run, and deliver those environments to partner labs.

Responsibilities:

• 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