Jobs 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.

Lead post-training research including SFT, RLHF/RLAIF, reward modeling, and evaluation suite design. Run rigorous experiments, curate preference data, scale data and training pipelines, identify failure modes, and partner with data, infrastructure, and engineering to ship improvements into production.

Design, build, and operate training, evaluation, and deployment pipelines for ML research. Translate prototype research code into production-grade systems, own data pipelines, implement observability, set engineering standards, and collaborate closely with researchers to ensure scalable, reliable experiments.

Design and run end-to-end ML experiments for autonomous research agents. Develop methods across RL, LLMs, multi-agent coordination, evaluate and scale results, and collaborate with engineers to productionize research.