Staff Software Engineer, ML Acceleration

Stack AV • Pittsburgh, PA

6d

About The Position

The ML Training Acceleration team is dedicated to increasing Stack AV's product development velocity by accelerating machine learning iterations. Our core mission is to deliver a training system that is reliable, scalable, user-friendly and observable. This involves profiling, optimizing, and fine-tuning our ML models, as well as evangelizing best practices and frameworks among Machine Learning Engineers (MLEs) across the company.

Requirements

• Education: Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field
• Experience: 5+ years of experience (including experience with GPU programming and optimization)
• Technical Skills: Strong programming skills in C++ and Python
• Proven experience in GPU programming and optimization
• Familiarity with deep learning frameworks, especially PyTorch
• CUDA programming
• Triton language for GPU kernels
• PyTorch optimization techniques
• TensorRT implementation
• ONNX model conversion and deployment
• Custom GPU kernel development
• Deep understanding of GPU architectures and performance optimization
• Problem-Solving: Strong analytical and problem-solving skills
• Communication: Excellent verbal and written communication skills, with the ability to convey complex technical concepts to non-technical stakeholders

Nice To Haves

• Autonomous vehicles (AV) experience is a bonus

Responsibilities

• Analyze ML models to identify and resolve performance bottlenecks.
• Incorporate OSS tools to enable ML engineers self-sufficiently profile and optimize models.
• Deliver solutions to streamline model deployment across various hardware platforms.
• Collaborate with ML researchers to balance model accuracy and speed.
• Implement optimizations using CUDA, Triton, and custom kernels.
• Promote Engineering Excellence: Maintain a high bar for engineering excellence in their own work but also set a culture of engineering excellence within the team.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level