Ingénieur(e) en mécanique industrielle chez AtkinsRéalis, déterminant les besoins des projets et concevant des solutions techniques. Évaluant les coûts et soutenant le développement d’équipe dans divers domaines.
About the role
- Autonomy Engineer working on planning and guidance for autonomous drones. Building algorithms for real-time flight decision-making under various environmental conditions.
Responsibilities
- 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.
- 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.
Requirements
- 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.
- 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.
- 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.
- 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.
- Comfortable with cloud infrastructure (GCP preferred), Docker, Git.
Benefits
- 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.
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