Principal Engineer, Autonomy
New
J
JobgetherAutonomous Systems
United StatesFull-TimePrincipal
Salary not disclosed
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Job Details
- Experience
- 15+ years
- Required Skills
- PythonC++Deep Learning
Requirements
- 15+ years of hands-on experience developing production-grade autonomous systems.
- Deep expertise across multiple autonomy domains, including perception, prediction, planning, localization, or controls.
- Proven success delivering autonomy software deployed on real-world vehicles at meaningful scale.
- Previous experience serving as the senior-most individual contributor within an autonomy engineering organization.
- Expert-level knowledge in at least one core autonomy discipline, with strong understanding of adjacent systems.
- Advanced programming skills in C++ and Python.
- Strong knowledge of deep learning techniques for autonomous systems, including model training, deployment, evaluation, and lifecycle management.
- Experience with ROS or ROS 2 and distributed computing environments for real-time autonomous systems.
- Strong understanding of software architecture, system integration, and production engineering practices.
- Excellent problem-solving, communication, and technical leadership skills.
- Demonstrated ability to influence technical direction across teams without formal management responsibilities.
Responsibilities
- Lead the technical vision and development of core autonomy functions, including perception, prediction, planning, or a combination of these domains.
- Design and evolve perception systems covering object detection, classification, tracking, and multi-modal sensor fusion.
- Develop advanced prediction models for intent inference, behavior forecasting, and complex edge-case handling.
- Architect planning and decision-making systems capable of supporting safe and efficient autonomous operations in dynamic environments.
- Define technical direction across module interfaces to ensure a cohesive, high-performing autonomy stack.
- Own the functional software architecture of the autonomy platform while collaborating with adjacent engineering teams.
- Evaluate and integrate deep learning techniques where they provide measurable performance improvements.
- Drive production-quality software development with a strong focus on reliability, scalability, and real-world deployment.
- Mentor senior engineers through technical leadership, code reviews, architectural guidance, and best practices without direct people management.
- Translate ambiguous technical challenges into executable engineering plans and deliver production-ready solutions.
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