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Added 1 hour ago
Senior Electrical Engineer
JDS Energy & Mining
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Promote continuous improvement by identifying opportunities to improve safety, quality, constructability, and project performance.
Added 1 hour ago
Electrical Engineer
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Promote continuous improvement by identifying opportunities to improve safety, quality, constructability, and project performance.
Added 1 hour ago
Electrical Engineer
JDS Energy & Mining
Wells, BC
Promote continuous improvement by identifying opportunities to improve safety, quality, constructability, and project performance.
Added 1 hour ago
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Added 1 hour ago
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Quality Engineer Lead
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- Full-time
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Added 3 hours ago
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Mine Engineer/Planner - PL#43148
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Added 3 hours ago
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Senior Software Engineer, Behavior Planning
AeroVect
Remote, Canada
- $160,000 to $200,000 CAD / year
- Full-time
- Remote
Contribute to the architecture and continuous improvement of behavior planning software
Added 3 hours ago
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Added 4 hours ago
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Added 6 hours ago
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AeroVect

Senior Software Engineer, Behavior Planning

Remote, Canada

Remote

$160,000 to $200,000 CAD / year · Full-time

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Job details

Pay: $160,000 to $200,000 CAD / year
Job type: Full-time
Workplace: Remote
Experience: Minimum of 3 years of industry experience in autonomous driving, robotics, or a related field
Posting language: English
Posted: Oct 23, 2026

Original job description

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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More about this job

Responsibilities

You will design and implement advanced behavior planning algorithms for autonomous vehicles operating in complex airport environments. This involves collaborating with cross-functional teams to ensure robust system integration and maintaining scalable code in C++ and Python.

Requirements

The role requires a minimum of 3 years of industry experience in autonomous driving or robotics and proficiency in modern C++ and Python. A Master's degree in Computer Science, Robotics, or a related field is also required.

Skills

C++
Python
Behavior planning
Autonomous driving
Robotics
State machines
Behavior trees
Probabilistic planning
Path planning
A*
RRT
ROS
ROS2
Optimization
Debugging
Profiling

Benefits

Equity

Remote locations

Toronto, Ontario, Canada
Vancouver, British Columbia, Canada

Remote locations (standardized)

Toronto, Ontario, Canada
Vancouver, British Columbia, Canada

Visa sponsorship

Not detected in the job text

Keywords

Autonomous driving
Behavior planning
C++
Python
Robotics
State machines
Behavior trees
Probabilistic planning
Path planning
A*
RRT
ROS
ROS2
Optimization
Debugging
Profiling
Machine learning
Decision-making
Multi-agent interaction
Simulation
Real-time navigation
Ground handling
Aviation
RaaS
Software engineering
Algorithm development
System architecture
Data analysis
Object-oriented programming
Latency optimization

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