---
title: 'Member of Technical Staff - Machine Learning Capabilities, New Graduates at Preference Model'
canonical: 'https://feeny.ai/job/member-of-technical-staff-machine-learning-capabilities-new-graduates-a3tbxd6kyjvy'
type: 'job'
last_seen: '2026-09-08'
---

# Member of Technical Staff - Machine Learning Capabilities, New Graduates at Preference Model

- **Company:** Preference Model
- **Location:** San Francisco, CA
- **Compensation:** $165k–$200k
- **Employment:** full-time
- **Work type:** onsite
- **Posted:** 2026-05-04
- **Last confirmed live:** 2026-09-08
- **Apply:** https://jobs.ashbyhq.com/preference-model/44642065-e592-44ba-810d-a019703463b6

## Job description

## ABOUT US

Preference Model is building automated ML research engineering.

Existing frontier models are brittle when applied to real-world ML tasks. The present bottleneck is the lack of high-quality RL training environments. Our first step is to build RL environments that reflect real-world complexity, with diverse tasks and robust reward functions.

Our founding team has previous experience on Anthropic’s data team building data infrastructure, and datasets behind Claude. We are partnering with leading AI labs to push AI closer to achieving its transformative potential.

## ABOUT THE ROLE

We’re hiring new graduate Machine Learning Engineers to design and build reinforcement learning environments to safely advance model capabilities in machine learning research and engineering. Specifically, you'll be teaching frontier models to do the work of an ML engineer or researcher at a frontier lab.

This role blends research and engineering. It will require you to stay up to date with the latest research, develop novel approaches, and realize them in code. You will have full ownership and autonomy of the environments you build. Your work will include designing and implementing RL environments, conducting experiments and evaluations, delivering your work into production training runs, and collaborating with other researchers and engineers.

You will join our Capabilities org, a small, high-ownership team and contribute directly to the data layer that powers frontier LLM capability.

Note: this role is for recent graduates only who can start soon.

## WHAT YOU WILL DO:

- Design and build RL environments and reward schemes that produce clean, learnable signals for frontier models on ML research and engineering tasks.
- Build deep expertise across the frontier of ML research, training, and inference infrastructure.
- Collaborate with others to brainstorm and create new ideas and tools to improve the environment building process.

WHAT WE ARE LOOKING FOR (QUALIFICATIONS):

- You have strong ML fundamentals and broad research interests. You read many papers or tutorials, understand topics deeply and have the creativity to translate them into RLVR problems.
- Expert knowledge in an active DL/ML research area, with publications or public code to show for it.
- Research experience (PhD, MS) is a strongly preferred.
- Deep understanding of transformer internals
- Proficiency in Python, Numpy, and systems programming; ideally PyTorch or JAX
- Smart problem solvers who take ownership and drives solutions end-to-end
- Passion for staying current with the rapidly evolving ML infrastructure landscape
- Ability to meet throughput expectations and respond quickly to feedback

## NICE TO HAVE:

- Strong expertise in kernel development (CUDA, Triton, Pallas), optimizing non-trivial neural modules to specific hardware
- Research projects, coursework, or personal work involving RL environments (any framework, any scale)
- Open-source contributions to ML infrastructure or RL tooling
- Experience with any cloud platform (AWS, GCP, Azure) or infrastructure-as-code tools

## WHAT WE OFFER:

- Competitive cash and equity compensation (>90th percentile)
- Ownership and autonomy in a fast moving startup environment
- Opportunity to work with top machine learning engineers
- Health, vision, dental, benefits
- 401K match
- Lunch provided everyday onsite
- Weekly snack orders
- Visa sponsorship & relocation support available

We value diverse perspectives and experiences. If you're excited about this role but don't check every box, we still encourage you to apply.

## About Preference Model

## Company Overview
- **One-liner**: Preference Model builds reinforcement learning (RL) environments that automate ML research and engineering, aiming to teach foundation models how to perform real-world ML tasks.
- **Entity Type**: Private (startup, pre-seed / seed stage – no funding publicly disclosed)
- **Headquarters**: United States (remote-friendly offices also in Canada)
- **Founded**: 2025
- **Founders**: Ning Cao and Jennifer Zhou

## Core Business
- **Primary industry**: Artificial Intelligence / Machine Learning Infrastructure
- **Target customers**: B2B – frontier AI labs and research teams building next-generation LLMs
- **Mission/purpose**: To teach models to perform ML research by creating high-quality RL training environments with diverse tasks and robust reward functions.

## Products & Services
- **Automated ML Research Engineering Platform**: A suite of RL training environments and tools designed to replace the current “brittle” application of frontier models on real-world ML tasks. The platform focuses on reward function design and task diversity to accelerate AI research.

## Market Standing
- **Valuation / Market Cap**: Not disclosed
- **Key Metric**: Total funding not publicly available; headcount is 1 (as of mid-2025) with monthly growth of +66.7%
- **Notable Investors / Partners**: The founding team previously built data and infrastructure at Anthropic, Stripe, and Datology; currently partnering with frontier AI labs
- **Growth Signals**: Website traffic grew +306.9% month-over-month (12,675 monthly visits); headcount growth of +66.7%; strong hiring signals on career page

## Competitive Advantages
- **Founding team pedigree**: Experience from Anthropic, Stripe, and Datology gives deep domain knowledge in data infrastructure and AI safety
- **Focus on a critical bottleneck**: RL environment quality is widely considered the main barrier to advancing frontier models – Preference Model directly addresses this
- **Small, focused team**: Enables rapid iteration and tight collaboration with partner labs

## Strategic Focus
- Automate the entire ML research engineering pipeline, moving from “how” to “what” questions in AI capability development
- Build robust, real-world RL environments that can scale across diverse domains
- Deepen partnerships with leading AI labs to shape the next generation of LLMs

## Why Work Here
- **Culture & impact**: “We are a small team committed to making big impact” – early-stage startup where every hire shapes the product
- **Work environment**: On-site workspace with offices in the United States and Canada (no remote/hybrid details disclosed, but likely flexible for the right talent)
- **Notable perks**: Not publicly listed, but as an early-stage AI infrastructure startup, employees can expect equity, direct influence on technical direction, and close collaboration with frontier AI researchers

## Sources
1. [preferencemodel.com](https://www.preferencemodel.com/) – Company overview, team, mission
2. [linkedin.com](https://www.linkedin.com/company/preferencemodel) – Company details, headcount, growth
3. [builtin.com](https://builtin.com/company/preference-model) – Year founded, office locations, industry tags
4. [github.com/preferencemodel](https://github.com/preferencemodel) – GitHub presence, creation date
5. [jobs.ashbyhq.com](https://jobs.ashbyhq.com/Preference-Model) – Active careers page

## Other roles at Preference Model

- [Member of Technical Staff - ML Infrastructure Engineer, Post-training](https://feeny.ai/job/member-of-technical-staff-ml-infrastructure-engineer-post-training-preference-re11f9e30efq) — San Francisco, CA
- [Member of Technical Staff - Research & Post-training](https://feeny.ai/job/member-of-technical-staff-research-post-training-preference-model-san-francisco-gtttnxrev7hv) — San Francisco, CA
- [Member of Technical Staff - Low Level & Kernels Capabilities](https://feeny.ai/job/member-of-technical-staff-low-level-kernels-capabilities-preference-model-san-4r3m43z2fz74) — San Francisco, CA
- [Member of Technical Staff - Machine Learning Capabilities](https://feeny.ai/job/member-of-technical-staff-machine-learning-capabilities-preference-model-san-ehsnrydyrvrs) — San Francisco, CA
- [Chief of Staff](https://feeny.ai/job/chief-of-staff-preference-model-san-francisco-evw9xnhwphed) — San Francisco, CA