MACHINE LEARNING ENGINEER (GENERAL)

The role

You will build and maintain ML systems and pipelines including training infrastructure, evaluation tooling, deployment, and observability. You will translate research code into reliable production infrastructure and own systems end-to-end.

What you will do

• Build and maintain training, evaluation, and deployment pipelines
• Take research code from prototype to production by refactoring, hardening, instrumenting, and testing
• Design observability into ML systems using metrics, logs, traces, and eval dashboards
• Own data pipelines for training and evaluation including ingestion, dedup, versioning, and validation
• Work closely with researchers to understand needs and identify slow or brittle processes
• Set engineering standards across the ML stack including testing, reviews, and runbooks
• Contribute to architectural decisions regarding research and production interaction

Required qualifications

• 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
• 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
• 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

About the company

makermaker.ai is a small, well-funded team building autonomous research agents for recursive self-improvement.