Senior Ml Research Engineer

ADOBE

Base: $172,500 -- $306,625 annually; bonus/equity:...
Gpu kernel development (triton/cuda)
Distributed training strategies (zero, tp, pp)
Inference acceleration techniques
You will take the ownership of implementing the low-level optimizations that make our foundation models faster, leaner, and more scalable

Job Summary

  • You will take the ownership of implementing the low-level optimizations that make our foundation models faster, leaner, and more scalable.
  • This role is designed for a high-potential engineer who is passionate about the intersection of hardware and AI.
  • Our compensation reflects the cost of labor across several U.S. geographic markets, and we pay differently based on those defined markets.

Matching Summary

You will take the ownership of implementing the low-level optimizations that make our foundation models faster, leaner, and more scalable.

Salary

Base: $172,500 -- $306,625 annually; Bonus/Equity: Not specified; Benefits: Not specified

Skills & Requirements

Must-have

  • GPU kernel development (Triton/CUDA)
  • Distributed training strategies (ZeRO, TP, PP)
  • Inference acceleration techniques
  • Performance profiling tools (PyTorch Profiler, Nsight)
  • Python and C++ proficiency
  • PyTorch or JAX experience
  • GPU architecture understanding

Nice-to-have

  • Open-source efficiency libraries contribution
  • Low-level communication libraries (NCCL)
  • Containerization (Docker/Kubernetes)
  • Modern model architectures (MoE, DiT)

Key Requirements

  • Master's or PhD's degree
  • Foundational Systems Programming
  • Hands-on ML Frameworks
  • GPU Awareness
  • Analytical Mindset

Work Rights

Not specified

Tailored Resume

Cover Letter