Staff ML Engineer, Dynamic World Perception

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 Dynamic World Perception team is responsible for real-time detection of road actors and properties. This includes developing occupancy networks and other perception algorithms to accurately identify and classify all dynamic elements within the driving environment. We're looking for a Staff or Senior Engineer to lead the development, maintenance, and enhancement of our long-range detection capabilities, ensuring the required safety and performance standards are met or exceeded. Responsibilities: Lead initiatives in the team to deliver state-of-the-art closed-and open-world detection capabilities. Contribute to expansion and fusion of existing detection models to support improving performance and extending performance at range. Contribute to improvement of model frameworks, architecture, and data pipelines. Develop robust software for onboard vehicle deployment, optimizing for real-time, resource-constrained, and safety-critical environments. Drive highly cross-functional features and workstreams from problem statement to delivery. Contribute to the technical direction of the team and broader perception org. Align stakeholders with strong presentation and communication skills. Lead challenging technical topics and build consensus. Qualifications: Extensive experience architecting, training, and deploying deep learning models into real-world environments. Track record of driving applied research projects from start to completion, including conception, problem definition, experimentation, iteration, and finally publication or productization. Experience delivering detection, segmentation, and/or tracking solutions for real-time robotic applications. Prior experience in sensor fusion is highly desirable. Strong experience in software engineering and machine learning algorithm design. Fluency in Python. Experience with C++. 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. Check out our Privacy Policy. 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.