The RoleWe're looking for a
Machine Learning Engineer who loves getting close to the metal. This is a hands-on engineering role focused on making models faster, more efficient, and more reliable through low-level optimizations and smart systems design.
The ideal candidate is excited by CUDA kernels, memory layouts, GPU scheduling, and squeezing performance out of complex training and inference workloads. They should be just as comfortable optimizing compute and networking paths as they are working alongside research teams to productionize new architectures.
This is a role for someone who enjoys deep performance tuning, understands the realities of running large-scale ML systems, and thrives in fast-moving, high-leverage environments.
Requirements- Strong background in systems-level ML engineering.
- Experience with CUDA, GPU kernel optimization, and performance tuning.
- Fluency in Python and at least one systems language (C++ or Rust preferred).
- Familiarity with distributed training frameworks (e.g., PyTorch, JAX, DeepSpeed, or similar).
- Experience working with large-scale training or inference infrastructure.
- Understanding of memory management, parallelization, and hardware-aware model optimization.
- 2+ years of experience working in ML infrastructure or performance-critical environments.
- Willingness to work in-person from our SF office in FiDi.
