---
title: 'RESEARCH ENGINEER (GENERAL) at MakerMaker'
canonical: 'https://feeny.ai/job/research-engineer-general-makermaker-san-francisco-wnsstd2cvhjs'
type: 'job'
last_seen: '2026-09-08'
---

# RESEARCH ENGINEER (GENERAL) 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/8a2375a5-91ad-4572-9d24-8ac78240c2bd

## 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 build and maintain the research systems and pipelines that our research runs on top of: data pipelines, training infrastructure, evaluation tooling, deployment, observability. The work bridges research and production, and you'll be the person who makes "we ran an experiment" actually mean "we ran it correctly, at scale, with results we trust."

You'll own systems end-to-end. You'll work with researchers daily and translate research code into infrastructure that the team can rely on. You'll move fast and you'll be measured on whether your systems make the team faster.

## WHAT YOU'LL DO

- Build and maintain the training, evaluation, and deployment pipelines that our research runs on
- Take research code from prototype to production: refactor, harden, instrument, test
- Design observability into our research systems (metrics, logs, traces, eval dashboards) so failures surface fast
- Own data pipelines for training and evaluation: ingest, dedup, version, validate
- Work closely with researchers to understand what they need, what's slow, and what's brittle
- Set engineering standards across our research stack (testing, reviews, runbooks) so the team scales
- Contribute to architectural decisions that shape how research and production interact

## WHAT WE'RE LOOKING FOR

- Senior research engineer with 6+ years building production-grade research systems
- Track record across the full lifecycle: data, training, evaluation, deployment, monitoring
- Strong distributed systems experience; you've shipped systems that have to be on
- Fluent Python, fluent with at least one of (PyTorch, JAX); comfortable at the systems-level when needed
- Comfortable with experimentation infrastructure (Ray, Slurm, Kubernetes, or similar)
- Bias toward shipping; you prefer working code over working diagrams
- Strong written communication

## NICE TO HAVE

- Experience building experimentation platforms or research infrastructure at a frontier research lab
- Background in distributed training systems
- Open-source contributions to research infrastructure
- History of working effectively with small senior teams

THIS ROLE IS PROBABLY NOT FOR YOU IF

- You want to do research with engineering as a side activity: this is engineering as the main thing
- Cross-functional work with researchers (translation, scoping, education) doesn't appeal
- Long-running ownership of running systems isn't appealing: this role has it

## 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

- [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, POST-TRAINING](https://feeny.ai/job/researcher-post-training-makermaker-san-francisco-werbh3m0mnf1) — 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