Senior ML Systems Engineer: Production Pipelines

MakerMaker; San Francisco (CA); Presencial; USD 180.000 - 260.000; Jornada completa

Descripción de la vacante

MakerMaker in San Francisco seeks a senior ML engineer to own end-to-end ML systems, from data pipelines to deployment. You will translate research code into reliable infrastructure, ship at scale, and help the team move fast with trustworthy results.

You will collaborate daily with researchers, design observability, implement testing and runbooks, and set engineering standards so our experiments translate into repeatable, scalable production workloads.

Formación

• 6+ years building production-grade ML systems.
• Experience across data, training, evaluation, deployment, monitoring.
• Strong distributed systems background.
• Fluent in Python and ML frameworks (PyTorch or JAX).
• Experience with experimentation infrastructure (Ray, Slurm, Kubernetes).

Responsabilidades

• 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).
• Own data pipelines for training and evaluation: ingest, dedup, version, validate.
• Work closely with researchers to understand needs and bottlenecks.
• Set engineering standards across our ML stack (testing, reviews, runbooks).
• Contribute to architectural decisions shaping research and production.

Conocimientos

Python

Distributed Systems

ML Engineering

Research Collaboration

Observability

Herramientas

PyTorch

JAX

Ray

Slurm

Kubernetes

Descripción del empleo

MakerMaker in San Francisco seeks a senior ML engineer to own end-to-end ML systems, from data pipelines to deployment. You will translate research code into reliable infrastructure, ship at scale, and help the team move fast with trustworthy results.

You will collaborate daily with researchers, design observability, implement testing and runbooks, and set engineering standards so our experiments translate into repeatable, scalable production workloads.