Senior AI Performance Engineer - GPUs & Distributed Systems

Fireworks AI

United States

On-site

USD 150,000 - 260,000

Full time

7 days ago

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Job summary

Fireworks AI is seeking a Software Engineer focused on Performance Optimization to push speed and efficiency across our AI infrastructure. You will own optimization at every layer, from GPU kernels to large-scale distributed systems.

Collaborating with research, infrastructure, and systems teams, you will target bottlenecks in LLMs, VLMs, and video models, improve latency and memory use, and drive CUDA, Triton, and PyTorch optimizations for production workloads.

Qualifications

Bachelor's degree in CS/CE/EE or equivalent practical experience.
5+ years of experience in performance optimization or HPC.
Experience optimizing large models for training and inference (LLMs, VLMs, or video models).
Proficiency in CUDA or ROCm and GPU profiling tools (Nsight, nvprof, CUPTI).
Familiarity with PyTorch and performance-critical model execution.
Experience with distributed systems debugging and multi-GPU optimization.
Contributions to open-source ML or HPC infrastructure.
Knowledge of compiler stacks or ML compilers (e.g., torch.compile, Triton, XLA).
Familiarity with cloud-scale AI infrastructure and orchestration tools (Kubernetes).
Background in ML systems engineering or hardware-aware model design.

Responsibilities

Analyze and optimize performance across GPU kernels and distributed systems.
Profile bottlenecks and implement low-level optimizations using CUDA, Triton, and related tools.
Collaborate with ML researchers to co-design hardware-efficient model architectures.
Scale inference and training across multi-GPU, multi-node environments.
Build and maintain performance benchmarking and monitoring infrastructure.