Member of Technical Staff - Machine Learning Capabilities

Preference-ModelSan FranciscoEngineering

Apply on company site

Opens the official Preference-Model careers page

Salary

$200K – $350K

Job type

Full-time

Experience

Senior · 5+ yrs

Posted

11 Sept 2026

Status

Open · checked 7d ago

About this role

Preference-Model seeks a Member of Technical Staff for Machine Learning Capabilities in San Francisco to design reinforcement learning environments for frontier models. This position suits experienced machine learning engineers with a strong background in transformer models and reinforcement learning research.

Role Overview

Preference-Model is hiring a Member of Technical Staff for Machine Learning Capabilities based in San Francisco. The professional will operate within the Capabilities organization to create reinforcement learning environments and reward functions that train models to execute machine learning engineering and research duties.

Key Responsibilities

Create and implement reinforcement learning environments alongside reward functions to generate clear learnable signals for frontier models regarding machine learning engineering tasks.

Develop comprehensive expertise across the frontier of machine learning training, research, and inference infrastructure.

Partner with team members to generate and build novel ideas and tools that enhance the environment creation workflow.

Requirements

Possess five or more years of professional experience in machine learning or research focusing primarily on transformer models and large language models.

Demonstrate strong machine learning fundamentals and broad research interests with the ability to turn concepts into reinforcement learning verification problems.

Show proficiency in Python and systems programming alongside experience with PyTorch or JAX.

Maintain the capability to meet throughput expectations and adapt quickly to feedback.

Nice to Have

Hold expert knowledge in an active deep learning or machine learning research area with public code or publications, or possess research experience via a Master of Science or PhD.

Display a deep understanding of transformer internals, modern large language model training and inference, and experience utilizing inference libraries such as vLLM or SGLang.

Demonstrate strong expertise in kernel development including CUDA, Triton, or Pallas.

Possess prior experience building complex interactive reinforcement learning environments.

Skills and Tools

Python

PyTorch

JAX

Systems programming

vLLM

SGLang

CUDA

Triton

Pallas

Salary and Benefits

Cash compensation ranging from $200K to $350K with competitive equity

Performance-based bonus range of $0 to $300K+

Comprehensive health, vision, and dental benefits

401K match

Daily onsite lunch and weekly snack orders

Visa sponsorship and relocation support

About the Company

Preference-Model functions as a superintelligence data research company constructing reinforcement learning environments designed to train capable and aligned superintelligences. The organization is backed by sixteen million dollars in seed funding led by a16z, and the founding team brings prior data infrastructure experience from Anthropic building datasets for Claude.

How to Apply

Interested candidates should apply on the official company careers page.

Skills and tech stack

Python PyTorch JAX vLLM SGLang CUDA Triton Pallas LLM LLMs Reinforcement Learning Machine Learning

Questions candidates ask

What is the location of the role?

The position is located in San Francisco.

How many years of experience are required?

The role requires 5+ years of experience working in machine learning or research, primarily focusing on LLMs and transformer models.

What compensation and benefits are provided?

The salary ranges from $200K to $350K with equity and a performance bonus ranging from $0 to $300K+. Benefits include health, vision, and dental coverage, a 401K match, daily onsite lunch, weekly snacks, visa sponsorship, and relocation support.

Is visa sponsorship available?

Yes, visa sponsorship and relocation support are available.

Ready to apply?

The application is on the official Preference-Model careers page.

Apply on company site