Teams at MakerMaker.AI

Build and own production-grade machine learning systems spanning data, training, evaluation, deployment, and observability. Translate research prototypes into reliable infrastructure, establish engineering standards, maintain experimentation pipelines, and partner closely with researchers. The role requires strong distributed systems experience, Python expertise, familiarity with PyTorch or JAX, and experience with tools such as Ray, Slurm, or Kubernetes.

Build, operate, and optimize production inference systems for large models. Own throughput, latency, cost-per-token, reliability, profiling, optimizations (quantization, custom kernels), observability, autoscaling, load testing, incident response, and postmortems.

Research and develop autonomous multi-agent systems that plan, decompose tasks, choose experiments, evaluate outputs, and recover from failures. Design methods, coordination patterns, and evaluation frameworks for open-ended research workflows; run rigorous experiments, ship methods to production, and drive team research direction.