Posted 14 days ago
Senior Software Engineer, Behavior Planning
@ AeroVect
Overview
AeroVect is hiring a Senior Software Engineer to design and build behavior planning systems for autonomous vehicles operating in airside ground-handling environments. The role owns key modules of the decision-making layer, spanning multi-agent interaction, rule-based and learned planning, and edge-case handling.
Pay:
$160,000–$200,000 a year
Responsibilities:
- Develop behavior planning algorithms for autonomous vehicles
- Design and maintain C++ and Python planning code
- Test in simulation and real-world scenarios
- Analyze performance and implement enhancements
- Document code, algorithms and system designs
Requirements summary:
- Master's degree in Computer Science, Robotics or related field
- Minimum 3 years of industry experience in autonomous driving or robotics
- Proficient in modern C++ and object-oriented programming
- Knowledge of state machines and behavior trees
- Familiarity with A*, RRT or optimization-based planning
Tools & skills
Source and dates
Employer career site / ATS feed · ashby
- Employer posting date
- 2026-10-23
- First seen by our source
- 2026-10-23
- Source record updated
- 2026-10-23
These dates describe the source record, not a live verification that the employer is still hiring. Check the original posting.
Job details
| Seniority | Senior |
| Department | Software development |
| Experience | 3+ years |
| Hours per week | 40 (estimated) |
| Work setting | Office, Client sites |
| Position | Mostly sitting |
| Physical demands | physical low · computer high |
| Education | Master’s degree (required) |
| Fields of study | Computer Science, Robotics |
Summary and details written by AI from the posting; the employer’s posting is the authority.
AeroVect
Saved company profile
Historical company information from a saved The Jobs API profile (LinkedIn/Crunchbase fields). It is not live-verified; figures may have changed. Missing values remain unknown.
The Airport Automation Company
Full company description
AI-powered airport logistics automation solutions, trusted by the world's largest airlines and airports.
- LinkedIn followers
- 5,376
- People associated on LinkedIn
- 63
- Reported office locations
- 233 E Harris Ave, South San Francisco, California 94080, US
- Toronto, Ontario, CA
- Atlanta, Georgia 30308, US
- Frankfurt, DE
- New York, 10007, US
- Business categories
- Air Transportation
- Robotics
- Software
- Logistics
- Transportation
- Artificial Intelligence (AI)
- Aerospace
- Autonomous Vehicles
- Manufacturing
- Other recorded names
- AeroVect
- Specialties
- Aviation
- Autonomous Driving
- Aerospace
- Transportation
- Logistics
- Airport Automation
- Physical AI
- and Artificial Intelligence
- Profile identifies a recruitment agency
- No
- Reported total investment
- 27,000,000
Currency and observation date were not supplied; this is not a current valuation or a funding round.
AI-powered airport logistics automation solutions, trusted by the world's largest airlines and airports.
| Field | Value |
|---|---|
| Industry | Airlines and Aviation |
| Company size | 51-200 employees |
| Headquarters | South San Francisco, California |
| Organization type | Privately Held |
| Year founded | 2020 |
| Specialties | AviationAutonomous DrivingAerospaceTransportationLogisticsAirport AutomationPhysical AIand Artificial Intelligence |
| Open jobs on Jobsy | 15 |
| View page |
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
Journal
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