Machine Learning Engineer
MakerMaker
Machine Learning Engineer
MakerMaker
Platform to build self-improving AI agents
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, well-funded team that works onsite in San Francisco.
ABOUT THE ROLE
You'll build and maintain the ML 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."
This is a senior ML engineering role. 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 ML 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 ML stack (testing, reviews, runbooks) so the team scales
- Contribute to architectural decisions that shape how research and
production interacts
WHAT WE'RE LOOKING FOR:
- Senior ML engineer with 6+ years building production-grade ML 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 ML lab
- Background in distributed training systems
- Open-source contributions to ML 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
Platform to build self-improving AI agents
MakerMaker.AI builds a platform for creating AI agents that can recursively improve themselves, enabling users to inject deep domain knowledge so agents specialize and outperform generalist models on niche problems. Its system lets users define improvement goals, guide the self-improvement process, and choose which internal models to use, including rewriting prompts to boost performance. The initial emphasis is on faster, more cost-effective inference to accelerate the self-improvement loop, with a focus on helping organizations deploy open-weight AI models more efficiently. Compared with others, it differentiates itself by enabling a diverse ecosystem of domain-specific intelligences through democratized RSI and human-guided optimization, rather than a monoculture of large general-purpose models. The goal is to democratize recursive self-improvement and empower users to build custom, high-performing AI agents tailored to their own data and workflows.
Explore more opportunities at MakerMaker