Sensor Modeling & Simulation Engineer (M&S)

Dominion Dynamics•Ottawa, ON

1d•Remote

About The Position

This role owns the sensor and electronic-warfare models end to end — RF/radar, EW, multi-sensor fusion, and synthetic imagery — in a long-range, air-centric context. The fidelity and determinism of these models underpin the quality of everything built on top of them, so this is a high-autonomy, system-level seat for someone who wants the whole sensing problem, not a slice of it.

Requirements

4+ years of professional systems programming in C++ and/or Rust, with real attention to performance, memory, concurrency, and deterministic behaviour — including hands-on Rust, or readiness to work primarily in Rust.
Understanding of sensor physics (cameras, LiDAR, radar, EO/IR) and how to model them accurately in simulation.
Depth in at least one of — and appetite to own all of — RF/radar modeling, electronic warfare, and multi-sensor fusion, plus comfort with the underlying math (link/detection budgets, estimation and tracking).
Mission-aligned: motivated to build sovereign Canadian defence capabilities and shape the future of allied autonomy programs.

Nice To Haves

Prior sensor M&S work in aerospace/defence — radar, EW, EO/IR, or fusion; beyond-visual-range or air-to-air modeling.
EW depth (jamming/deception, ESM/ELINT, ECCM) or fusion depth (IMM/PDA tracking, TDOA/FDOA geolocation).
Synthetic-data or RL training pipelines; integrating models via gRPC, DDS, ZeroMQ, or Zenoh; game-engine or flight-dynamics simulation (Unreal, DCS, X-Plane).

Responsibilities

Model RF and radar sensing for long range. Build and tune active-radar and imaging-radar models for beyond-visual-range air-to-air and air-to-surface geometry.
Model the electronic-warfare fight. Build the contested-spectrum side: passive intercept and emitter classification, jamming and deception, counter-countermeasures, and decoy effects.
Own multi-sensor fusion. Combine active, passive, and cooperative sensors into a single tracked world picture, including the estimation and track management behind it.
Model sensor physics across the spectrum, at varying fidelity. Model cameras, EO/IR, LiDAR, and radar from their underlying physics, delivered at multiple fidelity tiers from fast approximations to high-fidelity synthetic imagery.
Engineer for real-time, deterministic performance. Implement the stack in Rust and/or C++, fast enough for real-time and accelerated-time simulation and reproducible run to run.
Integrate plugins and third-party capabilities. Fold external engines, renderers, and libraries into the platform, working around or fixing bugs as needed.
Iterate on fidelity and bridge sim-to-real. Work with autonomy and other teams to integrate synthetic imagery, and continuously tighten sensor fidelity and environmental realism.