Technical lead evaluating hardware platforms and co-designing transformer models for on-device deployment. Leads team building low-level inference stack, optimizing for latency, memory, and power constraints. Requires deep experience with accelerators, transformers, and performance-critical ML software.
445k – 445k
HybridML Engineering
About the role
In this role, you will:
Evaluate and select silicon platforms (GPUs, NPUs, and specialized accelerators) for on-device and edge deployment of OpenAI models.
Work closely with research teams to co-design model architectures that meet real-world deployment constraints such as latency, memory, power, and bandwidth.
Analyze and model system performance, identifying tradeoffs between model design, memory hierarchy, compute throughput, and hardware capabilities.
Partner with hardware vendors and internal infrastructure teams to bring up new accelerators and ensure efficient execution of transformer workloads.
Build and lead a team of engineers responsible for implementing the low-level inference stack, including kernel development and runtime systems.
Run through the necessary walls to take nascent research capabilities and turn them into capabilities we can build on top of.
You might thrive in this role if you:
Have experience evaluating or deploying workloads on GPUs, NPUs, or other specialized accelerators.
Understand the performance characteristics of transformer models, including attention, KV-cache behavior, and memory bandwidth requirements.
Have designed or optimized high-performance compute systems, such as inference engines, distributed runtimes, or hardware-aware ML pipelines.
Have experience building or leading teams working on low-level performance-critical software such as CUDA kernels, compilers, or ML runtimes.
Have already spent time in the weeds teaching models to speak and perceive.
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