---
title: 'RESEARCHER, EFFICIENT INFERENCE at MakerMaker'
canonical: 'https://feeny.ai/job/researcher-efficient-inference-makermaker-san-francisco-9mk969hfxvc3'
type: 'job'
last_seen: '2026-09-08'
---

# RESEARCHER, EFFICIENT INFERENCE 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/c3677467-8f60-48bc-9997-e3720062e20d

## 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 be researching making models efficient: quantization, speculative decoding, sparse and structured attention, distillation, mixture-of-experts inference, and the training-time techniques that make those methods possible. The work spans algorithm design, careful evaluation, and pushing methods to where they actually run.

This is a senior research role with a clear engineering edge. You'll spend time at the intersection of model architecture and inference performance, designing methods that move accuracy/latency/cost trade-offs in our favor (then partnering with engineers to make those wins real in production).

## WHAT YOU'LL DO

- Research and develop quantization methods: post-training quantization, quantization-aware training, mixed-precision regimes, low-bit-width arithmetic
- Design and evaluate speculative decoding approaches: draft models, tree attention, parallel speculation, lookahead decoding
- Investigate training-time efficiency methods that compose well with inference: distillation, sparse attention, mixture-of-experts, low-rank adaptation, pruning
- Run controlled experiments at production scale; characterize what works on real workloads, not just toy benchmarks
- Co-design methods with the inference engineering team: push results to where they actually run, not stop at the paper
- Read deeply across the efficient ML / efficient inference literature; translate the most useful ideas into our stack
- Publish when the work warrants it; share findings internally
- Partner with model and training researchers so efficiency choices align with model architecture and post-training decisions

## WHAT WE'RE LOOKING FOR

- Strong track record of ML research on efficiency methods: quantization, speculative decoding, distillation, MoE, sparse attention, or adjacent
- 5+ years of hands-on research experience
- Deep familiarity with both training and inference performance characteristics
- Fluent in PyTorch, Jax or equivalent; comfortable working at the kernel and serving-framework level when methods require it
- Track record of moving efficiency research from prototype to production
- Strong statistical expertise: you'd notice a flawed comparison before someone else points it out
- Strong written communication
- Published research at NeurIPS, ICML, ICLR, MLSys, or comparable venues

## NICE TO HAVE

- PhD in ML, systems, or related field
- Open-source contributions to quantization, speculative-decoding, or efficient-inference libraries
- Experience with hardware-aware optimization and accelerator-specific tooling
- Background in numerical methods, low-precision arithmetic, or
- approximate computation

THIS ROLE IS PROBABLY NOT FOR YOU IF

- You want to focus on pretraining large models from scratch (that's a different role)
- You prefer abstract algorithmic research without hands-on implementation
- You want a fixed benchmark with stable targets (our targets shift with what our models actually need to do)

## 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, 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