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.

QUALIFICATIONS:

• Experience: 5+ years as a SWE, ideally building infrastructure/customer facing product, experience in AV or robotics is also great.
• Ideal Skills:
• Experience with both ML Platforms and building ML-based applications (bonus point if you have modeling experience).
• Experience building scalable, reliable infra at a fast-paced environment.
• Experience building or using ML infra built for a large number of customer teams.
• A deep understanding of design tradeoffs and ability to articulate those tradeoffs and work with others on getting alignment.
• Experience with building ML models or ML infra in the domains of autonomous vehicles, perception, and decision making (desirable but not required).
• Experience with model training, model optimization, or large data processing pipelines.
• Machine Learning Expertise is preferred but not necessary.
• Knows how to push the GPU to its limit from Python to CUDA kernel level.
• Built the inference or training loop for a large model (ideally with LLM flavor).
• Shipped ML products (NLP, computer vision, recommender systems, etc.) at scale to make business impact.
• Knows how to build low latency / high throughput batch or stream processing pipelines.
• Knows how to write (readable) high performance C++.
• Prior AV experience.
• Desired Attributes:
• High customer empathy, able to communicate with customers well
• Comfortable reading papers / keeping up with SOTA ML literature
  

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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

ABOUT THE ROLE:

The ML Training Team’s core mandate is training models as fast as possible for the company. The team’s main focus is ensuring our models have 100% gpu utilization and can scale linearly from 8 gpus -> 256 gpus. We also invest in tooling to empower our MLEs, by building profiling/debugging tools, setting up efficiency monitoring and integrating our trainer into our experiment management system.

RESPONSIBILITIES:

• Setup efficiency monitoring for all our training jobs to identify models that need improvement
• Work with customer teams to benchmark/profile their jobs and make improvements
• Create standardized APIs for stack-wide abstractions like training datasets, bulk inference jobs, evaluation metrics
• Optimize dataloaders / training data formats to ensure high gpu utilization
• Optimize distributed training configurations (network topologies, sharding strategies, pipelines, etc).