Machine Learning Engineer

About XORXOR is a platform that helps world-class companies pushing the frontier of AI hire exceptional ML, RL, and AI engineering talent.About Our ClientOur client is a well-funded AI startup working on next-generation training systems for large language models. The team is small, technical, and moving fast, with a strong focus on hands-on engineering over process.About the RoleThis team designs and builds training tasks that safely advance model capabilities in machine learning research and engineering - specifically, teaching frontier models to do the work of an ML engineer or researcher. The role blends research and engineering: staying current with the latest research, developing novel approaches, and realizing them in code, with full ownership and autonomy over what you build. Work includes designing and implementing training tasks, conducting experiments and evaluations, delivering work into production training runs, and collaborating with other researchers and engineers. This role is for experienced ML engineers (a separate track exists for new graduates).What You'll DoDesign and build training tasks and scoring functions that produce clean, learnable signals for frontier models on ML research and engineering tasksBuild deep expertise across the frontier of ML research, training, and inference infrastructureCollaborate with others to brainstorm and create new ideas and tools to improve the task-building processWhat We're Looking ForStrong ML fundamentals and broad research interests - you read many papers or tutorials, understand topics deeply, and have the creativity to translate them into rigorous, verifiable problemsProficiency in Python and systems programming, and at least one of PyTorch or JAXOwnership mentality and ability to drive solutions end-to-endPassion for staying current with the rapidly evolving ML infrastructure landscapeAbility to meet throughput expectations and respond quickly to feedbackNice to HaveExpert knowledge in an active DL/ML research area, with publications or public code to show for it - research experience (PhD, MS) is a big plusDeep understanding of transformer internals, training/inference of modern LLMs, experience with inference libraries (vLLM, SGLang, etc.)Strong expertise in kernel development (CUDA, Triton, Pallas)Experience building complex interactive evaluation or training environments