---
title: 'Member of Technical Staff, Alignment at Abundant'
canonical: 'https://feeny.ai/job/member-of-technical-staff-alignment-abundant-san-francisco-pg75c097z8qe'
type: 'job'
last_seen: '2026-11-06'
---

# Member of Technical Staff, Alignment at Abundant

- **Company:** Abundant
- **Location:** San Francisco, CA
- **Employment:** full-time
- **Work type:** onsite
- **Posted:** 2026-09-16
- **Last confirmed live:** 2026-11-06
- **Apply:** https://jobs.ashbyhq.com/abundant/0d11f5c1-036f-4ef9-872f-f9407fb79d57

## Job description

## ABOUT ABUNDANT

Abundant is an applied research lab focused on scaling reinforcement learning for safe and reliable agentic capabilities. We are an extremely talent-dense team of researchers, roboticists, founders, and operators whose work includes the Waymo Driver.

## THE ROLE

Frontier labs test their models in environments like ours, which means we are the first to see novel model behavior. You will make sure that alignment is baked into everything the do: the code we deploy, the runbooks we write, and the papers we publish.

## WHAT YOU’LL DO

- Run experiments on how our environments shape behavior: reward hacking, cheating the task, unsafe use of tools, deception over long runs. Then change how we build them.
- Break our own graders. Find the ways to score well without doing the work, close them, and keep the attacks as tests everyone runs.
- Build the tools that watch agents: monitors over trajectories, red team harnesses, and safety benchmarks that stay hard as models improve. On real runs, not toy ones.
- Keep our environments sealed. Network isolation, escapes, credentials, and how much damage an agent can do when it turns on us. Last summer’s failures came from here.
- Choose a research question about oversight, control, or long-horizon autonomy, answer it, and publish. This is part of the job, not something you do at night.
- Decide what we will not build. Tell partners plainly what they get from us is and is not safe for.

## WHO YOU ARE

- You are an engineer first. You build your own test pipelines, learn strange codebases quickly, and find the bug in the log.
- You know LLM safety in depth: attacks, red teaming, monitoring and control evaluations, reward hacking.
- You can turn a vague worry about a model into an experiment, run it, and say what the result does and does not prove.
- You read transcripts closely. You catch the small thing that makes the whole result wrong.
- You have put research into production systems, especially post-training, distillation, or evaluation that something depends on.
- You write clearly enough that your results change what other people do.
- You can decide fast with incomplete evidence and defend the call with data.

## NICE TO HAVE

- Published work on control, dangerous capability evaluations, oversight, or interpretability.
- RLHF or RLAIF experience, and a view on how training choices show up in behavior.
- You built or ran a public benchmark or agent task suite.
- You have run many agents at once: sandboxes, containers, logging.
- You have advised on AI safety or governance policy.

## COMPENSATION

Base Salary $250,000 - $450,000 Cash Bonus Sizable performance bonus tied to project and company milestones Equity Generous early-stage grant

## Benefits

Health, dental, vision + flexible PTO

## About Abundant

## Company Overview
- **One-liner**: Abundant provides reinforcement learning environments and high-quality training datasets for the world’s leading AI labs and enterprises, aiming to become the core infrastructure for AGI data.
- **Entity Type**: Private (Pre-Seed; Y Combinator Fall 2024 batch)
- **Headquarters**: San Francisco, California, United States
- **Founded**: 2024
- **Founders**: Jesse Hu, Ke Huang, Meji Abidoye

## Core Business
- **Primary industry/industries**: Artificial Intelligence (Training Data Infrastructure), Reinforcement Learning, Simulation
- **Target customers**: B2B – Top global AI labs (3 of the top 6), Fortune 500 enterprises, and robotics/AI startups
- **Mission or purpose statement**: “Working towards an abundant future—one where everyone has access to infinite intelligence, services and goods.”

## Products & Services
- **RL Environments & Datasets**: Custom simulation environments and curated datasets designed for reinforcement learning, imitation learning, and safe AI alignment. Used by leading labs to train agents and handle edge cases.
- **API-based Workforce**: An on-demand, global network of over 500 domain experts accessible via API to generate training data at scale, ensuring 100% reliability.

## Market Standing
- **Valuation/Market Cap**: Not disclosed
- **Key Metric**: Total Funding – $500,000 (Pre-Seed round led by Y Combinator, December 2024)
- **Notable Investors/Partners**: Y Combinator (lead investor)
- **Growth Signals**:
  - Headcount grew ~775% year-over-year to 20 employees (LinkedIn, as of mid-2025)
  - Generating billions of training tokens per month with 2x month-over-month growth
  - Already powering 3 of the top 6 global AI labs and multiple Fortune 500 enterprises
  - Talent sourced from top tech firms (e.g., Waymo, Google, Meta, AWS)

## Competitive Advantages
- **Deep domain expertise**: Founding team includes former ML engineers from Waymo, Google, Meta, and AWS with direct experience in self-driving, human computation, and large-scale data pipelines.
- **Exclusive expert network**: Proprietary network of 500+ domain specialists allows rapid creation of high-quality, rare datasets that are difficult to replicate.
- **Positioning as “NVIDIA of training data”**: Addresses the emerging bottleneck of data scarcity for RL and physical AI, a market with massive asymmetric upside.

## Strategic Focus
- **Scaling data for RL and agentic AI**: Moving beyond supervised fine-tuning (SFT) to “learning from experience” using reinforcement learning, synthetic data, and imitation learning.
- **Enabling AGI and physical AI**: Current priorities include expanding datasets for robotics, multimodal models, and autonomous systems to bridge the gap between digital chatbots and real-world agents.

## Why Work Here
- **High-impact, early-stage environment**: Small team (20 people) with flat structure, offering significant ownership over projects that directly shape AGI infrastructure.
- **Culture**: Described as “make shit happen” – action-oriented, with a focus on good food and good company. Remote-friendly (San Francisco / Remote US).
- **Technical focus**: Heavy emphasis on RL research, platform engineering, and product management roles – ideal for engineers who want to solve the core data bottleneck in AI.
- **Career growth**: Rapidly scaling company with strong YC backing; employees can accelerate into leadership or future founder roles (one job posting is titled “Chief of Staff (Future Founder/VC)”).

## Sources
1. [Abundant.ai – Official Website](https://www.abundant.ai/)
2. [Y Combinator – Abundant Company Page](https://www.ycombinator.com/companies/abundant)
3. [LinkedIn – Abundant Company Profile](https://www.linkedin.com/company/abundant-ai)
4. [Startuply.vc – Abundant Profile](https://startuply.vc/startup/abundant-1g8r98)
5. [Abundant Careers – Jobs Portal](https://jobs.ashbyhq.com/abundant)

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