Senior ML Systems Engineer - Production Pipelines

MakerMaker.AI; San Francisco, Northern (CA, KY); Hybrid; USD 180,000 - 270,000; Full time

Job summary

MakerMaker.AI is building autonomous research agents and scalable ML infrastructure in San Francisco. The senior ML engineer will own the ML stack end-to-end, from data pipelines and training to evaluation, deployment, and observability.

You will partner with researchers daily to translate prototype research into reliable, scalable systems that accelerate experimentation. You will move fast, instrument thoroughly, and be measured on building systems that make the team faster, more trustworthy,

Qualifications

• Senior ML engineer with 6+ years building production-grade ML systems.
• Track record across data, training, evaluation, deployment, monitoring.
• Strong distributed systems experience; shipped systems that run continuously.
• Fluent Python with at least one of PyTorch or JAX; comfortable at the systems level.
• Experience with experimentation infrastructure (Ray, Slurm, Kubernetes) and tooling.

Responsibilities

• Build and maintain training, evaluation, and deployment pipelines for research.
• Refactor, harden, instrument, and test research code for production use.
• Design observability: metrics, logs, traces, eval dashboards to surface failures quickly.
• Own data pipelines for training and evaluation: ingest, dedup, version, validate.
• Collaborate with researchers to translate requirements into reliable infrastructure.
• Set engineering standards across the ML stack: tests, reviews, runbooks for scale.
• Contribute to architectural decisions shaping research and production interfaces.

Skills

Python

PyTorch

JAX

Distributed systems

Kubernetes

Ray

Slurm

Observability

Data pipelines

Tools

Kubernetes

Ray

Slurm

Job description

MakerMaker.AI is building autonomous research agents and scalable ML infrastructure in San Francisco. The senior ML engineer will own the ML stack end-to-end, from data pipelines and training to evaluation, deployment, and observability.

You will partner with researchers daily to translate prototype research into reliable, scalable systems that accelerate experimentation. You will move fast, instrument thoroughly, and be measured on building systems that make the team faster, more trustworthy,