Member of Technical Staff, Performance Optimization
Remote • San Mateo • FullTime
Posted 1y ago
Remote Work Policy
Fully remote
Employment Type
FullTime
Categories
Applied AI Engineer
About the job
Fireworks is seeking a Software Engineer focused on Performance Optimization to enhance the speed and efficiency of their AI infrastructure. This role involves optimizing performance across all levels of the technology stack, from low-level GPU kernels to large-scale distributed systems. The primary focus will be on maximizing the performance of demanding workloads such as large language models (LLMs), vision-language models (VLMs), and advanced video models. You will collaborate with research, infrastructure, and systems teams to identify and resolve performance bottlenecks, implement advanced optimizations, and scale AI systems for production use cases, directly influencing the speed, scalability, and cost-effectiveness of cutting-edge generative AI models.
Responsibilities
- Optimize system and GPU performance for high-throughput AI workloads across training and inference.
- Analyze and improve latency, throughput, memory usage, and compute efficiency.
- Profile system performance to detect and resolve GPU- and kernel-level bottlenecks.
- Implement low-level optimizations using CUDA, Triton, and other performance tooling.
- Drive improvements in execution speed and resource utilization for large-scale model workloads (LLMs, VLMs, and video models).
- Collaborate with ML researchers to co-design and tune model architectures for hardware efficiency.
- Improve support for mixed precision, quantization, and model graph optimization.
- Build and maintain performance benchmarking and monitoring infrastructure.
- Scale inference and training systems across multi-GPU, multi-node environments.
- Evaluate and integrate optimizations for emerging hardware accelerators and specialized runtimes.
Requirements
- Bachelor’s degree in Computer Science, Computer Engineering, Electrical Engineering, or equivalent practical experience.
- 5+ years of experience working on performance optimization or high-performance computing systems.
- Proficiency in CUDA or ROCm and experience with GPU profiling tools (e.g., Nsight, nvprof, CUPTI).
- Familiarity with PyTorch and performance-critical model execution.
- Experience with distributed system debugging and optimization in multi-GPU environments.
- Deep understanding of GPU architecture, parallel programming models, and compute kernels.
- Master’s or PhD in Computer Science, Electrical Engineering, or a related field (preferred).
- Experience optimizing large models for training and inference (LLMs, VLMs, or video models) (preferred).
- Knowledge of compiler stacks or ML compilers (e.g., torch.compile, Triton, XLA) (preferred).
- Contributions to open-source ML or HPC infrastructure (preferred).
- Familiarity with cloud-scale AI infrastructure and orchestration tools (e.g., Kubernetes) (preferred).
- Background in ML systems engineering or hardware-aware model design (preferred).