Autonomy Engineer - Montreal - Shearwater Aerospace

Description

We're Building Autonomous Flight Intelligence

Drones that think for themselves. Not pre-programmed routes—true autonomy where aircraft make intelligent decisions onboard, adapting to weather, obstacles, and airspace in real-time without human intervention.

We're building weather-aware flight optimization that delivers outcome-driven autonomy for the real world—increasing mission success by extending range, endurance, safety, and reliability.

You'll build the simulation engine that makes this possible.

The Opportunity

You'll join as a founding engineer on our autonomy stack, inheriting significant prior work and taking full ownership of where it goes next. Working with our CTO, you'll own the autonomy and guidance layer — the algorithms and systems that translate vehicle capabilities, environmental data, and mission objectives into optimal, adaptive flight behavior. This isn't academic path planning — it's production autonomy that has to work in the real world, under uncertainty, with real consequences on real aircraft.

What You'll Own

• Extend and harden our route planning and optimization engine — multi-objective, constraint-aware, weather-integrated
• Build real-time guidance logic that adapts plans as conditions evolve (wind shifts, airspace changes, degraded vehicle state)
• Define how weather intelligence translates into actionable flight decisions
• Design planning abstractions that bridge high-level mission objectives and executable vehicle commands
• Own the interface between the planning stack and simulation — ensuring plans are validated against physical reality before they execute on real aircraft

Why this matters: A mission that gets grounded, diverted, or fails mid-flight is a mission that didn't need to fail. Your work determines whether our autonomy stack can operate confidently in real-world conditions — not just favorable ones.

You Might Be a Fit If...

You've built planners that work outside the lab. You know the gap between an algorithm that works on a benchmark and one that holds up under real-world uncertainty, changing constraints, and edge cases. You've shipped planning systems that operators actually trusted.

Your code is production-grade, not research-grade. Strong software engineering is non-negotiable. You write planning systems that are maintainable, testable, and extensible — not monolithic solvers that only you can touch. You think about architecture, not just algorithmic correctness.

You reason clearly about trade-offs under uncertainty. Planning for autonomous flight means making principled decisions when you don't have perfect information. You're comfortable with probabilistic reasoning, risk modeling, and knowing when a conservative plan is the right plan.

You understand the physics well enough to constrain the planner correctly. You don't need to build the vehicle model, but you need to know what constraints are physically meaningful, what the sim is telling you, and when a "valid" plan is actually unachievable in practice.

You're comfortable with ambiguity. Mission requirements evolve. New vehicle capabilities change the constraint space. You ask sharp questions, propose solutions, and deliver incrementally rather than waiting for perfect specs.

Technical Foundation We're Looking For

Planning & optimization:

• Demonstrated experience with path planning and trajectory optimization — graph-based, sampling-based (RRT/RRT*), or direct optimization methods
• Strong understanding of constrained and multi-objective optimization
• Experience building planners that operate under uncertainty and dynamic constraints
• Familiarity with how environmental data (weather, terrain, airspace) integrates into planning problems

Software engineering — this is the bar:

• Strong C++ and/or Python — you write code others want to maintain and extend
• Ability to design planning systems as clean, testable, well-interfaced software — not just algorithmic prototypes
• Experience with performance-sensitive systems where planning latency matters

Domain knowledge:

• Working knowledge of flight mechanics sufficient to reason about vehicle constraints
• Experience with UAV systems, autonomous vehicles, or aerospace applications is a strong plus
• Familiarity with meteorological data and how atmospheric conditions affect flight planning is a bonus

Infrastructure:

• Comfortable with cloud infrastructure (GCP preferred), Docker, Git

Nice to have:

• Robotics motion planning background (MoveIt, nav2, or similar)
• Experience with reinforcement learning or learned planners as complements to classical methods
• Degree in Engineering, Computer Science, Robotics, or related field — but we'll prioritize what you've built over credentials

What We Offer

Equity ownership – You're building foundational systems. You should own a meaningful piece of what we're creating.

Architectural influence – There's real prior work to build on, and real decisions still to make. You'll shape the planning architecture alongside the CTO as we push toward production-grade autonomy.

Hybrid flexibility – Work where you're most productive. We're in Montreal but value focus time and thoughtful collaboration over face-time.

Direct impact – Your algorithms run onboard aircraft making real flight decisions. You'll see your work extend operational capabilities in conditions where lesser systems get grounded.

Strong in planning, optimization, or autonomy but don't check every box? We want to hear from you. We value first-principles thinking and engineering craft over credential collection.

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