Autonomy & Machine Learning Engineer

Autonomy & Machine Learning Engineer

Position Details

• Location:

  Squamish, British Columbia, or remote within Canada

• Employment Type:

  Permanent Full-Time

• Salary Range:

  CAD $120,000–$165,000 per year

• Travel:

  Occasional travel to support flight testing, field operations, and demonstrations

• Eligibility:

  Candidates must be Canadian citizens or permanent residents of Canada.

Position Summary

The Autonomy & Machine Learning Engineer is responsible for developing, extending, and validating autonomous and perception capabilities for FireSwarm's aerial systems.

Working within a multidisciplinary engineering team, this role designs, implements, tests, and deploys software that enables aircraft to perceive their environment, support operator decision-making, execute mission objectives, and operate safely in complex conditions. The role focuses on practical autonomy, computer vision, and machine learning problems including environmental perception, mission execution, degraded communications, operational resilience, and field validation. Successful candidates will be comfortable moving between simulation, software development, flight testing, and operational analysis to deliver reliable capabilities for real-world missions.

Key Responsibilities

Autonomous Systems Development

• Design and implement bounded autonomous mission behaviours.

• Develop software supporting mission execution, operator decision support, and increasing levels of operational autonomy.

• Contribute to and extend perception, state estimation, and environmental understanding capabilities across FireSwarm autonomy systems.

• Develop, evaluate, and deploy computer vision and machine learning capabilities supporting detection, classification, localization, tracking, and mission-effect evaluation.

• Support autonomous operation under degraded communications, uncertain sensor inputs, and changing environmental conditions.

• Develop and maintain mission-critical onboard software components.

• Implement technical mechanisms that satisfy operator-facing requirements for autonomy state, confidence, and explainability.

Simulation and Validation

• Develop, maintain, and execute simulation-based validation activities supporting autonomy and perception capabilities.

• Support software-in-the-loop and hardware-in-the-loop testing.

• Analyze flight logs, recorded mission data, and operational results.

• Investigate failures and implement corrective improvements.

• Define technical acceptance criteria and regression tests for new autonomous capabilities.

Systems Integration

• Integrate autonomy and perception capabilities with aircraft, payloads, sensors, communications systems, and ground systems.

• Support sensor integration, calibration, and coordinate-frame alignment.

• Work with communications, navigation, geospatial, and mission-management systems.

• Contribute to flight-test preparation, execution, and post-flight analysis.

Software Engineering

• Design and maintain production-quality software for operational aircraft systems.

• Participate in architecture discussions, design reviews, and technical planning.

• Write testable, maintainable, and well-documented code.

• Contribute to engineering standards, peer reviews, and automated validation practices.

Qualifications

Education

Degree in one of the following areas, or equivalent practical experience:

• Computer Science

• Software Engineering

• Robotics

• Aerospace Engineering

• Computer Engineering

• Applied Mathematics

Experience

Professional experience developing software in one or more of the following domains:

• Autonomous systems

• Robotics

• Computer vision

• Machine learning

• Aerospace systems

• Embedded systems

• Geospatial systems

Additional qualifications:

• Experience deploying software into operational or safety-conscious environments.

• Experience integrating software with physical systems, sensors, or vehicles.

• Experience validating software through simulation, hardware testing, field testing, or operational data analysis.

Experience with uncrewed aircraft, aviation, wildfire operations, emergency response, defence, or other mission-critical systems is considered an asset.

Technical Skills

Candidates should demonstrate strong software engineering ability and relevant experience in several of the following areas:

• Computer vision, machine learning, or applied AI

• Edge inference and constrained airborne compute

• Thermal, infrared, or multispectral imagery

• Sensor fusion and state estimation

• Real-time or embedded systems

• Software-in-the-loop or hardware-in-the-loop simulation

• Linux

• MAVLink, ArduPilot, PX4, or comparable vehicle-control platforms

• Geospatial data, coordinate systems, or georeferencing

Experience with every listed technology is not required. FireSwarm values demonstrated depth in relevant autonomy problems and the ability to work across system boundaries.

Competencies

Successful candidates will demonstrate:

• Strong software engineering discipline

• Practical systems thinking

• Ability to diagnose failures across software and physical systems

• Sound judgment under incomplete or ambiguous requirements

• Ownership and accountability

• Clear technical communication

• Commitment to safety, reliability, and operational effectiveness

Working Environment

FireSwarm develops aerial systems for demanding wildfire, emergency-response, public-safety, critical-infrastructure, and defence applications.

The Autonomy & Machine Learning Engineer will work across software development, simulation, systems integration, machine learning, and operational testing. The role requires practical collaboration with engineers, pilots, operators, and field personnel, with occasional travel to support flight testing and operational activities.