---
title: 'Member of Technical Staff - Research Engineer, Post-training at Preference Model'
canonical: 'https://feeny.ai/job/member-of-technical-staff-research-engineer-post-training-preference-model-san-ac0t4g7jnane'
type: 'job'
last_seen: '2026-10-20'
---

# Member of Technical Staff - Research Engineer, Post-training at Preference Model

- **Company:** Preference Model
- **Location:** San Francisco, CA
- **Compensation:** $200k–$350k
- **Employment:** full-time
- **Work type:** onsite
- **Posted:** 2026-10-19
- **Last confirmed live:** 2026-10-20
- **Apply:** https://jobs.ashbyhq.com/preference-model/d51ec060-4d85-446a-bb69-98f3d6daab58

## Job description

## About Us

Preference Model is a superintelligence data research company. We build RL environments for training capable, better-aligned superintelligences. Almost every aspect of how a model behaves is shaped by the reward signals it's trained on. The biggest problem with AIs today is that they don't always do what we intend. Sometimes that's because they aren't capable enough, and sometimes it's because they aren't aligned. Both problems come down to the training objectives. We're trying to address both of these issues in the most direct, highest leverage way available to us, which today means researching how to build RL environments better, not just for the AIs of today but for the AIs as they will be when they outsmart us. Over the past year, we've built RL environments for several frontier labs, and we're backed by $16M in seed funding led by a16z. Our founding team has previous experience on Anthropic’s data team building data infrastructure, and datasets behind Claude.

## About the Role

Models of the future will be able to train themselves on tasks that they are not good at. We are interested in investigating how far we can push the boundaries of self-directed learning. We are looking for machine learning Research Engineers or Research Scientists to push the frontier of post-training on large language models in a role that blends research and engineering, requiring you to implement novel approaches and shape research directions.

What You Will Do:

- Train and evaluate models on our proprietary RL environments to validate data quality, surface gaps in task coverage, and close the feedback loop between environment design and model capability.
- Architect and optimize our RL training infrastructure, from training abstractions to distributed experiment management, using frameworks like Verl, OpenRLHF, or similar. Help scale our systems to handle increasingly complex research workflows.
- Design, implement, and test training environments, evaluations, and methodologies for RL agents.
- Profile and optimize training runs end-to-end, from data loading through reward computation, to maximize experiment throughput and shorten the research iteration cycle.

## What We are Looking For

- Experience running end-to-end LLM post-training pipelines of models sizes at least 7B in size
- Proficiency in Python and PyTorch or JAX
- Experience with at least one modern RL training framework
- Experience building and operating ML infrastructure at scale

You may be a good fit if you also:

- Have experience evaluating model outputs and building reward or evaluation signals
- Stay current on post-training research and can translate papers into running code
- Have strong opinions (loosely held) about how to structure RL training code for reproducibility and fast iteration
- Can balance research exploration with engineering rigor
- Have strong systems design and communication skills

Candidates don't need a PhD or extensive publications. Some of the best researchers have no formal ML training and gained experience building industry products. We believe adaptability combined with exceptional communication and collaboration skills are the most important ingredients for successful startup research.

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 - AI/ML Infrastructure Engineer, RL Post-training](https://feeny.ai/job/member-of-technical-staff-ai-ml-infrastructure-engineer-rl-post-training-8q01hsa95vss) — San Francisco, CA
- [Member of Technical Staff - Research Engineer, RL Environments](https://feeny.ai/job/member-of-technical-staff-research-engineer-rl-environments-preference-model-49nssssc0rp3) — San Francisco, CA
- [Member of Technical Staff - Machine Learning Capabilities, New Graduates](https://feeny.ai/job/member-of-technical-staff-machine-learning-capabilities-new-graduates-a3tbxd6kyjvy) — San Francisco, CA