Join a leading AI lab's cutting-edge GenAI team to be at the core of the AI revolution, where your expertise fuels the development of the most advanced Large Language Models.
1\. Overview
Join a leading AI lab's cutting-edge GenAI team and help build foundational AI models from the ground up. We're seeking MLOps and ML systems engineers with hands-on experience in large language model infrastructure across any of four areas: accelerator kernel programming, performance profiling and trace analysis, debugging accelerated and distributed workloads, and high-throughput inference serving. Experience with JAX, XLA or TPUs is especially valuable. This role involves AI model training and evaluation work, including writing and assessing ML systems tasks and solutions to generate high-quality training data for frontier AI systems.
This is a fully remote role open to candidates worldwide. Depending on where you are based, your engagement will be arranged through Cincinnatus LLC or one of its trusted employer-of-record partners in your country, with the opportunity to be placed at a leading AI lab as part of their extended workforce. This is a 40-hour full-time engagement, with no conflicts/no other engagements.
2\. Key Responsibilities
- Design challenging tasks across kernels, profiling, debugging and inference serving, including adversarial problems that expose where frontier models fail on accelerator code (for example Pallas kernels on TPU), and write accurate, well-structured solutions to them.
- Guide research and engineering teams to close knowledge gaps and improve AI model performance on ML systems, compilers and training infrastructure topics.
- Evaluate ML systems tasks and solutions, and provide clear written technical feedback that stands up to expert review.
- Develop guidelines and detailed rubrics covering kernel optimization, profiler output interpretation, distributed systems reasoning, and serving throughput and latency trade-offs.