Planning & Control Engineer, Early Career

At Bot Auto, we are revolutionizing the transportation of goods with our cutting-edge autonomous trucks, enhancing the quality of life for communities around the globe. With the agility of a start-up and the wisdom of seasoned experts, Bot Auto boasts a team that has achieved numerous world-firsts and unparalleled innovations. United by a shared vision, we create miracles and propel the future of transportation. Join us and transform your dreams into reality.

We are looking for an early-career Planning & Control Engineer to help develop and improve the driving behavior of our autonomous trucks. This is primarily an algorithm and software development role. Working with experienced engineers, you will investigate driving problems, implement improvements, and evaluate their performance through simulation and vehicle data. You will also collaborate with our Validation team to develop targeted tests and identify regressions.
Rigorous experimental habits are central to this role. We value engineers who investigate why an approach works, understand where it fails, and draw conclusions supported by evidence—not just produce successful demonstrations.

This role is intended for recent graduates and engineers with approximately 0–3 years of relevant experience. Prior autonomous-driving industry experience is not required.

• Develop planning and control software. Implement and improve algorithms in behavior planning, motion planning, trajectory generation, or vehicle control, with assignments aligned with your technical strengths.
• Investigate real-world driving issues. Use vehicle logs, simulation, and code analysis to understand unexpected behavior, develop testable hypotheses, and investigate root causes with guidance from experienced engineers.
• Design and run meaningful experiments. Establish clear baselines, make deliberate comparisons, and choose scenarios and metrics that test the intended hypothesis. Distinguish genuine improvements from changes caused by unrelated factors.
• Make results reproducible and conclusions transparent. Maintain experiment scripts and records of relevant code, configurations, and datasets. Document what was tested, what was observed, and what remains uncertain.
• Analyze failures and regressions. Investigate counterexamples and performance trade-offs rather than relying only on successful cases or aggregate metrics. Turn findings into algorithm improvements and targeted regression tests.
• Collaborate on implementation and validation. Participate in design and code reviews, integrate changes into the autonomy stack, and work with Simulation and Validation engineers to assess performance.

• Demonstrated strong experimental habits: the ability to formulate a clear question, establish a baseline, design a comparison that meaningfully tests a hypothesis, and interpret results critically. You should be comfortable reporting negative results, identifying limitations, and revising your original hypothesis when the evidence does not support it.
• Strong programming fundamentals and hands-on experience in C++ and Python, including implementing, debugging, and testing algorithms.
• A solid foundation in at least one relevant area, such as motion planning, decision-making, optimization, robotics, or feedback control. Depth in one area is more important than familiarity with every area.
• Experience completing a substantive technical project that involved both implementation and evaluation. Research, internships, robotics competitions, and substantial academic or personal projects are welcome.
• Ability to communicate technical reasoning clearly, distinguish observations from assumptions, and explain both the strengths and limitations of a proposed solution.
• A bachelor’s degree or higher in Robotics, Computer Science, Electrical Engineering, Mechanical Engineering, Applied Mathematics, or a related field, or equivalent practical experience.

• Experience with closed-loop simulation, physical robots, or vehicle-based experiments.
• Familiarity with vehicle dynamics, system identification, or interactions between planning and control.
• Experience with decision-making under uncertainty, dynamic environments, or interactions with other road users.