The ideal Flight Controls Engineer at Precision AI is someone who can design, implement, and tune UAV flight control and autopilot software, then validate those changes quickly on real aircraft in the field. You’ll work in a multidisciplinary environment alongside embedded, mechanical, GNC (Guidance, Navigation, and Control), and software engineers, developing autopilot-based control strategies that directly impact UAV stability, safety, and spray performance from concept through flight testing.
You will primarily work within an ArduPilot-based autopilot stack, but we are open to candidates with deep PX4 or similar autopilot experience who have also spent meaningful time working with ArduPilot. You’ll spend your days inside the flight control firmware, designing and tuning control loops, analyzing flight logs, and iterating rapidly between bench tests, simulation, and live flights to improve platform behavior. Some days you’ll be extending or modifying flight modes and controllers; other days you’ll be in the field, adjusting parameters and validating performance under agricultural shock, vibration, wind, and dust.
This role will work out of our Calgary office due to the hands-on nature of testing, integration, and UAV flight verification and validation.
Control System & ArduPilot Design
- Design and tune attitude, position, and altitude control loops for multirotor, fixed-wing, and/or VTOL UAVs, with a focus on low-altitude agricultural missions.
- Develop and modify flight control modules (e.g., attitude control, motors/mixers, flight modes, failsafes) within our ArduPilot-based stack to support Precision AI’s airframes and mission profiles.
- Define control objectives and performance metrics tied to safety, stability, and spray accuracy in real-world field conditions.
- Implement control logic that accounts for non-linear aircraft behavior, disturbances, saturation, delays, and sensor noise.
- Support integration of control algorithms into embedded implementations in partnership with firmware teams, ensuring real-time and resource constraints are respected.
Modeling, Dynamics & Stability Analysis
- Build and maintain dynamic models of the UAV platform and subsystems (actuators, sensors, airframe responses) to inform control design and tuning.
- Perform stability analysis using time- and frequency-domain techniques (e.g., step responses, Bode plots) to converge on stable operating regions and robust tuning.
- Develop and validate assumptions around sensors and estimation (rate limits, latency, noise characteristics, update timing) and understand their impact on control performance.
- Use tools such as MATLAB or Python to prototype and evaluate control loops, tuning approaches, and non-linear behaviors before and alongside flight testing.
- Document modeling assumptions, control architectures, and tuning rationale for repeatable and auditable engineering decisions.
Simulation, Testing & Iteration
- Build and run simulations (including software-in-the-loop or equivalent) to de-risk control strategies before flight, while recognizing that final validation happens in the field.
- Design focused test plans for tuning and validation, including step responses, disturbance testing, and controlled flight envelope expansion.
- Support frequent field testing and iterative tuning under real agricultural environments (shock, vibration, wind, dust), with new code changes expected to fly on short time scales once basic checks are complete.
- Analyze flight logs and test data to identify instabilities, coupling effects, and performance bottlenecks, then implement improvements quickly in code and configuration.
- Define and refine failsafes, geofencing, and recovery behaviors to protect people, aircraft, and equipment while enabling aggressive low-altitude operations.
Cross-Functional Collaboration
- Work closely with Embedded Engineers to ensure sensor timing, real-time behavior, and hardware constraints match control intent and autopilot implementation details.
- Collaborate with Mechanical and Electrical teams to align actuator and sensor choices, mechanical layout, and power systems with stability and control requirements.
- Partner with flight test and field operations teams to plan, execute, and iterate on flight test campaigns, clearly communicating risk, mitigation strategies, and expected outcomes.
- 3–6 years of controls or autopilot engineering experience with demonstrated ownership of UAV flight control or autopilot software that has flown on real aircraft.
- Strong foundation in system dynamics, control theory, and stability analysis (time and frequency domain) applied to UAVs or similar aerospace systems.
- Significant hands-on experience with at least one open or proprietary UAV autopilot (such as ArduPilot or PX4), including modifying flight control or autopilot modules and using logs and parameters for diagnosis and iteration.
- At minimum, some direct experience working with ArduPilot (e.g., configuration, tuning, or code-level changes), and readiness to work primarily in an ArduPilot-based environment.
- Experience modeling dynamic systems and translating models into practical control implementations (for example, through system identification or similar hardware characterization techniques).
- Proficiency with MATLAB, Python, or similar tools for control prototyping, simulation, and analysis.
- Experience tuning controls on real vehicles (ideally UAVs), including field-based iteration with short cycles from code change to flight test.
- Comfort working in a fast-paced environment where, after basic bench and simulation checks, new control or autopilot code is expected to be flown promptly under controlled test conditions.
- Deep experience with ArduPilot or PX4 internals (flight modes, controllers, EKF behavior, failsafes, mission logic) beyond basic configuration and tuning.
- Experience tuning controls and autopilot behavior on UAVs used for agriculture, inspection, mapping, or other low-altitude, high-precision missions in challenging environments.
- Experience with VTOL or other non-standard or highly coupled airframes (tailsitters, hybrid lift, heavily loaded aircraft, or airframes with significant payload shifts).
- Familiarity with state estimation and sensor fusion concepts and how they interact with control performance, even if not your primary ownership area.
- Experience with real-time implementation constraints and embedded integration workflows on flight controllers such as Pixhawk-class hardware.
- Comfort analyzing large test datasets and building repeatable analysis or log-processing scripts to accelerate tuning and troubleshooting.
- Experience working on safety-critical or high-reliability systems where incremental change, clear rollbacks, and documented test plans are expected.
