---
title: 'RESEARCHER, POST-TRAINING at MakerMaker'
canonical: 'https://feeny.ai/job/researcher-post-training-makermaker-san-francisco-werbh3m0mnf1'
type: 'job'
last_seen: '2026-09-08'
---

# RESEARCHER, POST-TRAINING at MakerMaker

- **Company:** MakerMaker
- **Location:** San Francisco, CA
- **Employment:** full-time
- **Work type:** onsite
- **Posted:** 2026-05-18
- **Last confirmed live:** 2026-09-08
- **Apply:** https://jobs.ashbyhq.com/makermaker.ai/6b9de906-544e-44c3-98f9-fec11c59cd47

## Job description

## ABOUT THE COMPANY

We're building autonomous research agents for recursive self-improvement (multi-agent systems that propose, run, and analyze machine learning experiments). We're a small team based in San Francisco, on-site

## ABOUT THE ROLE

You'll lead our work on model post-training: supervised fine-tuning, preference data, reinforcement learning from human and AI feedback, reward modeling, and the evaluation suites that tell us what's actually working. You'll own a research area that meaningfully shapes our model behavior and capability.

This is a hands-on senior research role. You'll set direction, run experiments, and ship into production. You'll partner with the data, infrastructure, and engineering teams to make the post-training pipeline reliable and fast: improvements there compound into every model we ship.

## WHAT YOU'LL DO

- Lead post-training research: SFT, RLHF/RLAIF, RLVR, DPO and successor methods, reward modeling, preference data design
- Design and curate the data that goes into post-training (from sourcing, to filtering, to quality assessment)
- Build and maintain the evaluation suites that measure what matters; resist Goodharting your own benchmarks
- Run rigorous experiments (controls, ablations, statistical significance) and write up internal findings clearly
- Scale data pipelines and the infrastructure team to scale training
- Identify and characterize failure modes (reward hacking, distribution drift, eval saturation) and design experiments to address them
- Stay current on the post-training literature; bring useful methods in, ignore the noise

## WHAT WE'RE LOOKING FOR

- Strong track record of post-training research (SFT, RL, reward modeling) at a frontier-model lab or equivalent
- 5+ years of hands-on ML research experience
- Comfort with large-scale data curation and preference-data pipelines
- Experience designing evaluation suites for capabilities that aren't easily benchmarked
- Fluent in PyTorch or equivalent; comfortable at the scale of distributed training
- Strong statistical instincts: you'd notice a flawed comparison before someone else points it out
- Strong written communication

## NICE TO HAVE

- PhD in ML, statistics, CS, or adjacent
- Published research at NeurIPS, ICML, ICLR, COLM, RLC, or comparable venues
- Experience with reward hacking detection, scaling reward models, or RLHF infrastructure
- Synthetic data generation experience
- Background in RL math (policy gradients, importance sampling, off-policy methods)
- Open-source contributions to post-training infrastructure

THIS ROLE IS PROBABLY NOT FOR YOU IF

- You're primarily interested in pretraining (that's a different role)- You'd rather invent novel methods in isolation than ship them into a model that real users run
- You prefer benchmarks that are stable to evaluation work where the right answer isn't yet defined

## About MakerMaker

## Company Overview
- **One-liner**: MakerMaker.AI builds AI agents that autonomously build other AI agents, enabling the rapid creation and deployment of agentic systems.
- **Entity Type**: Private (Seed stage)
- **Headquarters**: San Francisco, California, United States
- **Founded**: 2024
- **Founders**: Dhaval Adjodah (CEO & Co-Founder), Owen He (CTO & Co-Founder)

## Core Business
- Primary industry/industries: Artificial Intelligence, Agentic AI, Enterprise Software
- Target customers: B2B, Enterprise (engineering and product teams looking to automate AI agent development)
- Mission or purpose statement: To build agents that build agents, accelerating the creation of autonomous AI systems.

## Products & Services
- **MakerMaker.AI Platform**: A proprietary AI system that autonomously designs, builds, and deploys other AI agents. The platform is designed to reduce the time and cost of agent development, enabling teams to create sophisticated agentic workflows without extensive manual coding.

## Market Standing
- **Valuation/Market Cap**: Not publicly disclosed
- **Key Metric**: Total Funding — Not publicly disclosed (Seed stage)
- **Notable Investors/Partners**: Not publicly disclosed
- **Growth Signals**: Headcount grew +300% YoY (from ~1 to 7 employees); team includes talent from Google DeepMind, Schmidt Sciences, Intel, and Insilico Medicine; presence in Canada, United States, and Poland; co-founders published an op-ed in The Washington Post on DeepSeek and open-source AI.

## Competitive Advantages
- **Founder expertise**: CEO Dhaval Adjodah and CTO Owen He have deep backgrounds in AI research and engineering, with connections to top AI labs.
- **Unique value proposition**: The "agents building agents" approach could dramatically lower the barrier to creating custom AI agents, a rapidly growing market.
- **Early mover in agentic AI**: MakerMaker is positioned at the cutting edge of the autonomous AI agent trend, which is a major focus for the industry in 2024-2025.

## Strategic Focus
- Scaling the platform and expanding the engineering team
- Recruiting top ML and engineering talent to accelerate product development
- Building a strong presence in the San Francisco Bay Area AI ecosystem
- Likely focused on proving product-market fit and securing a Series A round

## Why Work Here
- **Culture**: Small, high-impact team (currently ~7 people) with a strong technical focus — 62% of the workforce is in technical roles.
- **Remote/Hybrid/Office**: The team is distributed across Canada, the US, and Poland, but the company is hiring for a full-time role in San Francisco, suggesting a preference for in-person or hybrid collaboration at the HQ.
- **Notable perks**: Opportunity to work on one of the most ambitious problems in AI (building agents that build agents); direct collaboration with the founding team; steep learning curve and high ownership.
- **Engineering culture**: Likely research-heavy, fast-paced, and focused on pushing the boundaries of what AI agents can do. The talent pool includes alumni from Google DeepMind and Intel, indicating a high bar for technical skill.

## Sources
1. [makermaker.ai](https://makermaker.ai/)
2. [LinkedIn - MakerMaker.AI](https://www.linkedin.com/company/makermaker)
3. [RocketReach - MakerMaker.AI](https://rocketreach.co/makermakerai-profile_b6fdbc3fc646e3f0)
4. [AIJobs.com - MakerMaker AI Jobs](https://www.aijobs.com/companies/makermaker-7261448)
5. [LinkedIn - Owen He](https://linkedin.com/in/owen-he-b7b064279)

## Other roles at MakerMaker

- [RESEARCH ENGINEER (GENERAL)](https://feeny.ai/job/research-engineer-general-makermaker-san-francisco-wnsstd2cvhjs) — San Francisco, CA
- [RESEARCHER, EFFICIENT INFERENCE](https://feeny.ai/job/researcher-efficient-inference-makermaker-san-francisco-9mk969hfxvc3) — San Francisco, CA
- [RESEARCHER, AGENTS FOR AUTOMATED DISCOVERY](https://feeny.ai/job/researcher-agents-for-automated-discovery-makermaker-san-francisco-a726565wtck7) — San Francisco, CA
- [RESEARCHER (GENERAL)](https://feeny.ai/job/researcher-general-makermaker-san-francisco-3jr63q9kj1g3) — San Francisco, CA
- [INFERENCE ENGINEER](https://feeny.ai/job/inference-engineer-makermaker-san-francisco-4jpp5x9m6488) — San Francisco, CA