Member of Technical Staff - ML Infrastructure Engineer, Post-training
About this role
This role suits experienced software engineers who want to build the compute and data systems that power large language model post-training in San Francisco. Preference-Model is hiring a Member of Technical Staff for ML Infrastructure in San Francisco to scale the infrastructure for in-house reinforcement learning environments.
Role Overview
Preference-Model seeks a Senior ML Infrastructure Engineer to design systems that support post-training research on large language models. The position focuses on creating scalable compute, scheduling, and data pipelines to accelerate reproducible experiments.
Key Responsibilities
Requirements
Skills and Tools
Salary and Benefits
About the Company
Preference-Model is a superintelligence data research company backed by $16M in seed funding led by a16z. The organization builds reinforcement learning environments to train capable and aligned artificial intelligence models.
How to Apply
Interested candidates should apply on the official company careers page.
Skills and tech stack
Questions candidates ask
What is the salary range for this role?
The salary ranges from $200K to $350K, with offers including equity and a performance-based bonus.
Where is the position located?
The position is located in San Francisco.
Is visa sponsorship available?
Yes, visa sponsorship and relocation support are available.
What frameworks and tools are required?
Required tools and frameworks include PyTorch, JAX, vLLM, SGLang, Megatron, Slime, veRL, Ray Train, SkyRL, AWS, GCP, and Kubernetes.
What type of company is Preference-Model?
Preference-Model is a superintelligence data research company that builds reinforcement learning environments for training aligned artificial intelligence models.
The application is on the official Preference-Model careers page.
Apply on company site