---
title: 'Member of Technical Staff - ML Infrastructure Engineer, Post-training at Preference Model'
canonical: 'https://feeny.ai/job/member-of-technical-staff-ml-infrastructure-engineer-post-training-preference-re11f9e30efq'
type: 'job'
last_seen: '2026-09-08'
---

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

- **Company:** Preference Model
- **Location:** San Francisco, CA
- **Compensation:** $200k–$350k
- **Employment:** full-time
- **Work type:** onsite
- **Posted:** 2026-08-25
- **Last confirmed live:** 2026-09-08
- **Apply:** https://jobs.ashbyhq.com/preference-model/e3f2cf5d-b8aa-4efa-b7fd-b1481f3f56c2

## 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

Frontier research moves only as fast as its infrastructure permits. Building solid infrastructure is foundational to our mission of pushing self-directed learning as far as it can go.

We are looking for Senior ML Infrastructure Engineers to build the infrastructure and systems that power the frontier of post-training on large language models. This role involves building scalable infrastructure to enable high-throughput systems and shape how our research is run, bringing us closer to models that can train themselves on what they aren't yet good at.

## WHAT YOU WILL DO

- Design, build, and scale the compute, scheduling, and data infrastructure that powers post-training research on our in-house RL environments
- Develop and maintain core ML framework primitives and internal tooling that researchers rely on daily, accelerating reproducible experimentation and reducing time from idea to result
- Build evaluation and benchmarking infrastructure, monitoring, logging, and debugging tooling, and automated testing and deployment systems, so failures are caught early and infrastructure stays reliable as it scales
- Partner directly with Research Engineers to translate research needs into infrastructure requirements, and ship fast in response to their feedback

## WHAT WE ARE LOOKING FOR

- Strong software engineering fundamentals and hands-on experience building production-grade LLM inference and training infrastructure (ideally from the ground up)
- Experience building LLM training/inference internals such as transformers, distributed training, and working on inference libraries like vLLM, SGLang, Megatron
- Experience working on RL training frameworks like Slime, veRL, Ray Train, SkyRL
- Significant experience and understanding of distributed systems principles, and have hands-on experience with cloud platforms (AWS, GCP) and container orchestration (Kubernetes), building systems for high-throughput, low-latency workloads
- Have experience with data engineering tools and building robust, scalable data pipelines
- Proficiency in core ML frameworks such as PyTorch or JAX
- Can balance production rigor with the pace of fast-moving research, and communicate infrastructure tradeoffs clearly to researchers who aren't infra specialists

## WHAT WE OFFER:

- Competitive cash and equity compensation (>90th percentile)
- Ownership and autonomy in a fast moving startup environment
- Opportunity to work alongside senior and staff engineers from frontier labs and infrastructure companies, plus top ML 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 - 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
- [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