Senior Software Engineer, Behavior Planning

Aerovect · posted Oct 23, 2026

Open to candidates in Canada

CA$160k–CA$200k / yr (≈ $113k–$142k) · Full-time · Senior

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What this job asks for

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Must have

  • •Proficient in modern C++ (11/14/17)
  • •Skilled in Python
  • •Deep understanding of behavior planning algorithms
  • •Familiarity with path planning algorithms
  • •Minimum of 3 years of industry experience

Nice to have

  • •Knowledge of state machines, behavior trees
  • •Expertise in path planning algorithms
  • •Knowledge of machine learning techniques
  • •Experience with ROS / ROS2
  • •Implementing systems that can re-plan at high frequencies

Tools and technologies

C++, Python, ROS, ROS2

Read the full posting

Who We Are

AeroVect is transforming ground handling with autonomy, redefining how airlines and ground service providers around the globe run day-to-day operations. We are a Series A company backed by top-tier venture capital investors in aviation and autonomous driving. Our customers include some of the world’s largest airlines and ground handling providers. For more information, visit www.aerovect.com .

We are looking for an experienced Senior Software Engineer who can design and build best-in-class behavior planning systems for autonomous driving in structured, low-speed environments.

In this role, you'll own the design and implementation of key modules in the behavior planner — the decision-making layer that determines what the vehicle should do in complex, dynamic airside scenarios. You'll work at the intersection of mission-level goals and motion-level execution, tackling problems in multi-agent interaction modeling, rule-based and learned decision-making, and robust handling of edge cases unique to airport ground operations.

This opportunity offers a deeply technical engineer the chance to shape a market-defining enterprise product that combines autonomous vehicle technology with a robotics-as-a-service (RaaS) business model. This role reports to our Planning Tech Lead and works closely with the autonomy engineering team.

You Will

  • Develop and implement advanced behavior planning algorithms for autonomous vehicles

  • Collaborate with cross-functional teams to ensure robust integration and functionality of planning systems

  • Design, write, and maintain efficient and scalable code in C++ and Python

  • Contribute to the architecture and continuous improvement of behavior planning software

  • Conduct extensive testing in simulated environments and real-world scenarios to validate and refine behavior planning algorithms

  • Analyze system performance and implement enhancements based on data and feedback

  • Maintain comprehensive documentation of code, algorithms, and system designs

  • Work closely with other engineering teams to ensure seamless coordination and development

You Have

  • Proficient in modern C++ (11/14/17) and object-oriented programming

  • Skilled in Python for rapid prototyping and testing

  • Strong in debugging, profiling, and optimizing code

  • Deep understanding of behavior planning algorithms such as state machines, behavior trees, and probabilistic planning

  • Familiarity with path planning algorithms like A*, RRT, or optimization-based methods

  • Master’s degree in Computer Science, Robotics, or a related field

  • Minimum of 3 years of industry experience in autonomous driving, robotics, or a related field

We Prefer

  • Knowledge of state machines, behavior trees, and decision-making under uncertainty

  • Expertise in path planning algorithms such as A*, D*, and Rapidly-exploring Random Trees (RRT)

  • Knowledge of machine learning techniques, especially in the context of behavior prediction and planning

  • Experience with ROS / ROS2

  • Implementing systems that can re-plan at high frequencies to adapt to dynamic changes in the environment

  • Ensuring that behavior planning algorithms can execute with minimal latency for real-time navigation

  • Proficiency in optimization techniques and probabilistic models for making informed planning decisions under uncertainty

  • Master’s degree or PhD in Robotics, AI, Mathematics, or a related field with a focus on planning, optimization, or control theory is a plus

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