Staff Software Engineer, ML Acceleration

Stack AV • Pittsburgh, PA

5d

About The Position

About Stack: Stack is developing revolutionary AI and advanced autonomous systems designed to enhance safety, reliability, and efficiency of modern operations. Stack's autonomous technology incorporates cutting-edge advancements in artificial intelligence, robotics, machine learning, and cloud technologies, empowering us to create innovative solutions that address the needs and challenges of the dynamic trucking transportation industry. With decades of experience creating and deploying real world systems for demanding environments, the Stack team is dedicated to developing an autonomous solution ecosystem tailored to the trucking industry's unique demands. About the Role: 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

• 5+ years of experience (including experience with GPU programming and optimization)
• 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
• Strong analytical and problem-solving skills
• Excellent verbal and written communication skills, with the ability to convey complex technical concepts to non-technical stakeholders
• Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field

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