ML Engineer (Training Infra), Foundational Models

Build, maintain, and continually push the limits of our distributed training stack across large GPU clusters.

Design and implement parallelism strategies — data, tensor, pipeline, sequence, expert — and reason about which combinations make sense for which architectures and which…

Profile and optimize end-to-end training throughput: kernel performance, communication overlap, memory layout, checkpointing, data loading.

Write and tune custom GPU kernels (CUDA, Triton) where off-the-shelf is leaving performance on the table.

BS or MS in Computer Science or a closely related technical field (or equivalent demonstrated experience).

Hands-on experience training large models with distributed training frameworks — Megatron-LM, DeepSpeed, FSDP, NeMo…

Deep working knowledge of GPU architecture, the CUDA programming model, and standard tools for profiling GPU workloads (Nsight, PyTorch profiler, etc.).

Strong PyTorch internals knowledge. You're comfortable reading and modifying low-level training code, not just using high-level APIs.