Gpu Kernel Engineer (AI)

TL;DR

Gpu Kernel Engineer (AI): Develop and optimize high-performance GPU kernels for machine learning workloads with an accent on CUDA programming, kernel optimization, and advanced GPU features. Focus on designing efficient GPU operations, quantization techniques, and performance profiling to accelerate AI model inference.

Location: San Francisco, New York, USA

Salary: $185K–$250K

Company

hirify.global powers inference for leading AI companies by combining applied AI research, flexible infrastructure, and developer tooling, backed by $150M Series D funding.

What you will do

• Design and implement high-performance GPU kernels for ML operations such as matrix multiplications and attention mechanisms
• Optimize code using CUDA, PTX assembly, and architecture-specific techniques
• Apply advanced performance optimizations including memory coalescing and tensor core acceleration
• Implement features like quantization (FP8/FP4), sparsity, and compute/communication overlap
• Identify and resolve performance bottlenecks using profiling tools
• Collaborate with research teams to productionize advancements and contribute to open-source GPU libraries

Requirements

• 1–5 years of CUDA development experience
• Strong understanding of GPU architecture and programming paradigms
• Proficiency in C++ and GPU performance profiling tools
• Knowledge of CUDA C++ API, memory access patterns, numerical precision, and modern GPU features
• Location: Must be able to work onsite in San Francisco or New York
• English: B2 level or higher

Nice to have

• Experience with Transformer models and attention optimization
• Familiarity with GPU kernel libraries like Cutlass, Triton, Thrust, CUB
• Background in GEMM tuning and distributed/multi-GPU compute
• Contributions to open-source GPU projects and research publications

Culture & Benefits

• Competitive compensation with meaningful equity
• Full medical, dental, and vision insurance coverage for employee and dependents
• Generous PTO including company-wide Winter Break
• Paid parental leave and company-facilitated 401(k)
• Exposure to diverse ML startups and networking opportunities