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 efficient inference methods (quantization, speculative decoding, sparse attention, MoE, distillation), run production-scale experiments, evaluate trade-offs, and partner with engineers to deploy improvements and publish results.