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 Perception team is looking for a lead to build up a team developing and deploying perception components for next generation self driving systems. This requires developing strong ownership over critical components to being able to understand, experiment, improve, and field state-of-the-art perception systems in real time, safety critical applications. We are focused on building a product so candidates should have a mission-driven mindset to deliver a product, which entails significant cross-functional interaction and customer-centric obsession.

Responsibilities:

  • Identify and develop next generation perception architectures.
  • Produce a coherent data representation of the environment based on data fusion between map alignment, tracking, drivable area, etc.
  • Work cross-functionally across perception, mapping, and decision making to ensure autonomy-wide alignment. 
  • Partner with other teams across the company to identify key requirements, dependencies, and prioritize the key use cases that support business outcomes. This includes leading across data collection ops, ML infrastructure, cloud infra, onboard infra, and other partners to ensure e2e delivery of solutions.

Qualifications:

  • PhD (or 5+ years of experience) in perception related fields, such as Robotics, Computer Vision, Machine Learning.
  • 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 perception/tracking solutions for real-time robotic applications.
  • Excellent performance-oriented C++ engineering skills.
  • Experience with automated driving.
  • Experience working with a GPU (cuda).
  • Experience in Deep Learning pipelines. #LI-JG1

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.

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