---
title: 'INFERENCE ENGINEER at MakerMaker'
canonical: 'https://feeny.ai/job/inference-engineer-makermaker-san-francisco-4jpp5x9m6488'
type: 'job'
last_seen: '2026-09-08'
---

# INFERENCE ENGINEER 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/4a70e4bd-ee2c-4a83-9ad0-13702d68519b

## 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 build and operate the inference systems that serve our models in production. The work spans serving infrastructure, runtime optimization, and the long tail of production infrastructure that come with running real workloads.

This is an engineering role, not a research role. You'll measure, profile, debug, and ship. You'll work alongside researchers, but your job is to make their work fast and reliable in production. Real ownership, real autonomy.

## WHAT YOU'LL DO

- Build, operate, and harden production inference systems serving large models at high throughput
- Own the performance characteristics of those systems end-to-end: throughput, latency, cost-per-token, reliability under load
- Profile real workloads to identify bottlenecks; ship fixes that move the metric you set out to improve
- Implement and integrate inference optimizations from the research team (quantization, custom kernels, scheduling improvements, memory management) into production
- Design observability into the inference layer: metrics, tracing, alerting that surface regressions before users notice them
- Run capacity planning, autoscaling, and load testing for varied workload shapes (batch, online, mixed, agentic)
- Diagnose and resolve production incidents; write postmortems that turn bugs into systemic fixes

## WHAT WE'RE LOOKING FOR

- Senior ML systems engineer with 3+ years building production-grade, large-scale serving infrastructure
- Strong distributed systems experience ; you've been on-call for systems that matter
- Performance profiling and optimization fluency: you read flame graphs, you are analytical and measured before you change
- Experience with GPU-accelerated inference at scale (multi-GPU, multi-node, batched and streaming workloads), preferably experience with AMD GPUs
- Fluent Python; comfortable reading and writing systems-level code in at least one of the following languages: C++,CUDA, ROCm or Triton
- Track record of shipping production infrastructure, preferably surfaces serving millions of requests across diverse workloads
- Good written communication; you can write a runbook that someone else can follow at 3am

## NICE TO HAVE

- Open-source contributions to inference / serving frameworks
- Experience with mixed cloud and on-premises deployments
- Familiarity with hardware-aware optimization (memory hierarchy, NCCL/RDMA, NUMA)
- Background in compilers, runtimes, or accelerator software stacks
- THIS ROLE IS PROBABLY NOT FOR YOU IF
- You're primarily a researcher, the work here is building, not exploring
- You want to focus narrowly on one component; this role spans the stack
- Production responsibility (incidents, on-call, ownership of running systems) isn't appealing

## 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, 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