CUDA Engineer

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Weekday (YC W21)

WebsiteLinkedInAll Job Openings

11 - 50 employees

Founded 2021

☁️ SaaS

🎯 Recruiter

Human Resources • SaaS • Recruitment

Weekday is a modern recruitment platform that combines AI technologies with a vast database of potential candidates, aiming to streamline the hiring process for companies in India. They offer various services, including a proactive outreach approach that helps employers connect with top talent, as well as tools for candidates to easily apply for jobs. Weekday's emphasis on candidate engagement through multiple channels, including email, WhatsApp, and phone calls, sets it apart in the competitive landscape of recruitment agencies.

📋 Description

• Analyze and optimize GPU kernels for performance, efficiency, and hardware utilization • Use profiler metrics such as L2 cache hit rate, L2 throughput, occupancy, and related signals to guide kernel improvements • Review GPU kernel implementations and identify bottlenecks without requiring extensive background in the underlying algorithms • Write, modify, and reason about C++17, Python, and GPU programming code • Apply CUDA, HIP, shader programming, or related kernel programming expertise to improve performance outcomes • Document optimization decisions clearly, including when specific profiler metrics are or are not useful

🎯 Requirements

• Available to work at least 20 hrs/wk • Fluent in core C++ features through C++17 • Working knowledge of Python and Git • Fluent in at least one GPU programming model, such as CUDA, HIP, Slang, HLSL, GLSL, or related kernel programming • At least 1 year of professional or graduate-level research experience working with GPUs • Strong understanding of GPU profiler performance metrics and how to use them to optimize kernels • Ability to optimize GPU kernels without needing deep prior context on every algorithm • Experience with CUDA, HIP, CUDA C++ Core Libraries, inline PTX assembly, or tensor core-level optimization is a plus • Experience optimizing kernels for NVIDIA Blackwell hardware is a plus • Familiarity with NSight Compute is a plus • Prior experience with GPU hardware organizations such as NVIDIA, AMD, or Qualcomm is a plus • Open-source contributions related to GPU kernel optimization are a plus