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MakerMaker

Platform to build self-improving AI agents

Research Engineer

Full-Time
No salary listed
Senior
San Francisco, CA, USA
In Person
AI & Machine LearningAI Research

About the job

Requirements
  • At least 6 years of experience building production-grade research systems.
  • Experience across the full lifecycle of data, training, evaluation, deployment, and monitoring.
  • Strong distributed systems experience, including having shipped systems that need to remain operational.
  • Fluency in Python and at least one of PyTorch or JAX; systems-level comfort when needed.
  • Comfort with experimentation infrastructure such as Ray, Slurm, Kubernetes, or similar.
  • Strong written communication.
Responsibilities
  • Build and maintain the training, evaluation, and deployment pipelines used for research.
  • Take research code from prototype to production by refactoring, hardening, instrumenting, and testing it.
  • Design observability into research systems, including metrics, logs, traces, and evaluation dashboards, so failures surface quickly.
  • Own data pipelines for training and evaluation, including ingesting, deduplicating, versioning, and validating data.
  • Work closely with researchers to understand their needs and identify what is slow or brittle.
  • Set engineering standards across the research stack, including testing, reviews, and runbooks.
  • Contribute to architectural decisions shaping how research and production interact.
  • Own systems end-to-end and translate research code into reliable infrastructure.
Desired Qualifications
  • 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.

About the company

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.

Company Size

11-50

Company Stage

N/A

Total Funding

N/A

Headquarters

N/A

Founded

N/A

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