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. 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.
Qualifications:
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
Autonomous vehicles (AV) experience is a bonus
We are proud to be an equal opportunity workplace. We believe that diverse teams produce the best ideas and outcomes. We are committed to building a culture of inclusion, entrepreneurship, and innovation across gender, race, age, sexual orientation, religion, disability, and identity.
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Please Note: Pursuant to its business activities and use of technology, Stack AV complies with all applicable U.S. national security laws, regulations, and administrative requirements, which can restrict Stack AV’s ability to employ certain persons in certain positions pursuant to a range of national security-related requirements. As such, this position may be contingent upon Stack AV verifying a candidate’s residence, U.S. person status, and/or citizenship status. This position may also involve working with software and technologies subject to U.S. export control regulations. Under these regulations, it may be necessary for Stack AV to obtain a U.S. government export license prior to releasing its technologies to certain persons. If Stack AV determines that a candidate’s residence, U.S. person status, and/or citizenship status will require a license, prohibit the candidate from working in this position, or otherwise be subject to national security-related restrictions, Stack AV expressly reserves the right to either consider the candidate for a different position that is not subject to such restrictions, on whatever terms and conditions Stack AV shall establish in its sole discretion, or, in the alternative, decline to move forward with the candidate’s application.
