Staff Machine Learning Engineer, Vehicle Behaviors
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 Team
The Vehicle Behaviors team is responsible for improving the decision-making capabilities and reliability of the AV in real world scenarios. Working broadly across domains and planning components, this team owns everything from ML-based Agent Predictions and Ego Trajectory Generation to safety-critical and heuristic-driven components like Trajectory Selection, to off-board metrics and tools used to measure performance and prevent regressions. This team has some of the most visible, high-impact work on the AV’s performance and plenty of challenging problems still to solve.
About the Role
We are looking for strong machine learning engineers to help in developing and deploying motion planning components for next generation self-driving systems. This requires strong ownership over critical components that cross domain boundaries. Candidates need to understand, experiment, improve, and field state-of-the-art machine learning systems in real-time, safety-critical applications. We are focused on building a product. Candidates should have a mission-driven mindset and customer-centric obsession to deliver a compelling product, and be able to work with significant cross-functional interactions. You might be a good fit for this role if you have experience with any of the following:
- Deploying state of the art ML models or architectures to solve planning problems on fielded products
- Refining ML outputs for use in down-stream planning components
- Integrating ML-based planning techniques side-by-side with heuristic approaches in production
- Building data mining, data introspection, or other critical ML tooling
What Success Looks Like
Responsibilities:
- Design, scope, implement, and integrate machine learning systems to solve on-vehicle behavior problems in a real-time, resource-constrained environment.
- Develop tooling and infrastructure necessary to measure and iterate on ML models
- Provide input in the technical direction for the team, and work cross-functionally to develop safe systems. This will include working closely with other teams such as perception, localization, and controls to ensure that the input to the motion planning modules is appropriate.
- Work closely with systems engineers to ensure a safe, well tested product is delivered.
- Work closely with verification teams to ensure proper testing and validation of the motion planning modules. Make extensive use of unit testing, simulation, and log simulation to properly validate their work.
- Collaborate with other autonomy teams including perception, localization, controls, etc. to ensure solutions are appropriate for real-world performance
- Provide input to team roadmaps and ensure product features are properly prioritized.
- Identify bottlenecks and limitations in system performance, and develop novel motion planning components to unlock new capabilities and ensure a reliable system.
- Be involved in experimentation, design and iteration exercises, and help to align stakeholders by using strong presentation and communication skills.
Experience:
- Strong Python skills
- Experience with key ML libraries like PyTorch and TensorRT
- Experience with cloud orchestration tools like Flyte
- Exceptional written and verbal communications skills
- Experience developing code for real-world robotics/semi-autonomous platform
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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.