ML Engineer AI Trainer (LLM Performance)

About the role

A leading AI lab's GenAI team is looking for MLOps and ML systems engineers to help train its large language models. You'll write and review hard ML systems tasks (kernels, profiling, debugging and inference serving) that become training data for frontier AI. This is a full time, 40 hours a week remote contract open worldwide, a strong fit for Filipino ML engineers with hands on LLM infrastructure experience. Depending on where you live, you'll be engaged through Cincinnatus LLC or one of its local employer of record partners. AI Alerts may earn a referral fee if you're hired through this link. It costs you nothing and doesn't change how we pick or rank jobs.

What you'll do

• Design challenging tasks across kernels, profiling, debugging and inference serving, including problems that show 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 give 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 tradeoffs.
• Work with other subject matter experts to keep training data consistent and accurate.

Requirements

• 2+ years of hands on professional experience in ML systems, ML infrastructure, model serving, or accelerator performance engineering. This is a hands on systems role, not an applied modelling or data science one.
• Practical experience in at least one of these (more than one is a strong plus): writing or optimizing custom kernels (Pallas, Triton, CUDA, CUTLASS); performance profiling and trace analysis (JAX/XLA profiler, Kineto, torch.profiler, Nsight); debugging distributed or accelerator bound workloads; serving large language models at scale (vLLM, SGLang, TensorRT LLM, JetStream, KV cache, paged attention, continuous batching).
• Working production experience with JAX and/or PyTorch. Framework level depth is a strong plus: custom operators, sharding and distributed training (pjit/shard_map, FSDP, Megatron, MaxText), or compiler and graph level work (XLA/StableHLO, torch.compile, MLIR).
• Experience with modern accelerators such as TPU (v4 to v6e), H100, B200, Trainium or MI300, and the ability to reason about throughput, latency and memory tradeoffs.
• Demonstrable career progression.
• Able to work reliably for at least 40 hours a week on weekdays, with no other engagements.
• Strong written communication and the ability to explain complex technical decisions clearly.

About Mercor

Mercor partners with leading AI labs and enterprises to train frontier AI models using human expertise. Experts on Mercor work remotely on projects that train and improve AI systems, and get paid weekly through Stripe or Wise.

Visit Mercor →

AI Alerts shares third-party job opportunities for informational purposes only. We are not the employer and are not involved in the hiring process. Always verify the company and role through official channels before applying, and never pay to apply, train, onboard, process documents, or secure a job offer. Legitimate employers do not ask applicants for money. Read our Terms to learn more.