Member of Technical Staff, Performance Optimization

About the role

We're looking for a Software Engineer focused on Performance Optimization to help push the boundaries of speed and efficiency across our AI infrastructure.

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

Qualifications

• Master’s or PhD in Computer Science, Electrical Engineering, or a related field
• Experience optimizing large models for training and inference (LLMs, VLMs, or video models)
• Knowledge of compiler stacks or ML compilers (e.g., torch.compile, Triton, XLA)
• Contributions to open-source ML or HPC infrastructure
• Familiarity with cloud-scale AI infrastructure and orchestration tools (e.g., Kubernetes)
• Background in ML systems engineering or hardware-aware model design

Example Projects

• Implement fully asynchronous low-latency sampling for large language models integrated with structured outputs
• GPU kernels for the new low-precision scheme and run experiments to find optimal speed-quality tradeoff
• Build a distributed router with a custom load-balancing algorithm to optimize LLM cache efficiency
• Define metrics and build harness for finding optimal performance configuration (e.g. sharding, precision) for a given class of model
• Determine and implement in PyTorch an optimal sharding scheme for a novel attention variant
• Optimize communication patterns in RDMA networks (Infiniband, RoCE)
• Debug numerical instabilities for a given model for a small portion of requests when deployed at scale

Pay

Total compensation for this role also includes meaningful equity in a fast-growing startup, along with a competitive salary and comprehensive benefits package. Base salary is determined by a range of factors including individual qualifications, experience, skills, interview performance, market data, and work location. The listed salary range is intended as a guideline and may be adjusted. Base Pay Range (Plus Equity): $175,000 - $220,000 USD

Schedule

Flexible work schedule to accommodate global teams and diverse time zones.