• Collaborate with researchers to build systems-efficient training loops for cutting-edge speech and video models.
• Profile and optimize distributed training workloads across internal infrastructure.
• Design and implement improvements to in-house training framework to increase hardware utilization and throughput.
• Apply the latest algorithmic and systems-level techniques to improve scalability, reproducibility, and stability in large-scale training.
• Investigate and resolve performance bottlenecks across the ML stack—from dataloaders to kernel fusion to network latency.

Qualifications

You might thrive in this role if you:

• Have experience working with large-scale or multi-modal ML pipelines.
• Enjoy diving into the internals of deep learning frameworks and distributed systems to make them faster and more reliable.
• Are proficient in Python and have strong software engineering practices.
• Understand how training kernels work and how to optimize them (e.g., CUDA, Triton, XLA, or custom ops).
• Are passionate about making ML training stable at scale, not just functional.
• Care deeply about correctness, reproducibility, and engineering craftsmanship.

Benefits

• Help build one of the most technically advanced AI platforms in the world and shape how it reaches and supports users globally.
• Work directly with a world-class team of engineers and researchers solving frontier problems in speech and language AI.
• Have a voice in how the company grows, how customers succeed, and how AI transforms human communication.