Robotics Engineer

SBQuantum•Sherbrooke, QC

1d•Hybrid

About The Position

SBQuantum is a seed-stage startup at the forefront of magnetic sensing innovation. Our vision is to unlock the full potential of magnetic intelligence through our proprietary quantum diamond technology and curated algorithms. By combining diamond-based quantum sensors with advanced AI-driven software, we transform complex magnetic field data into actionable, high-value insights. Our solutions enable accurate magnetic mapping, navigation and object detection in environments where traditional sensing technologies—like GPS, radar, imagery, or sonar—cannot perform. From public safety and defence to space exploration, our multidisciplinary team of engineers, physicists, and data scientists is redefining how the world perceives magnetic signals—turning invisible complexity into clear, useful intelligence.

Requirements

5 years of experience in a startup environment
Background in robotics, including INS systems EKF filters tuning
Demonstrated experience customizing INS platforms with custom sensors
Experience with robotics control software such as MOSA, ASPN, PNTOS ArduPilot
Experience in autonomous platforms deployment
Comfortable wearing multiple hats and switching contexts quickly
Strong problem-solver with a bias toward action
Excellent communication skills (written and verbal)
High level of discretion and professionalism

Nice To Haves

Experience in deeptech, hardware, or scientific environments
Familiarity with compliance, grants, or government processes
Bilingual English/French

Responsibilities

INS filter design & tuning - Design, implement, and tune Kalman-based and hybrid filters (e.g. EKF, UKF) for inertial navigation systems, balancing convergence speed, noise rejection, and drift compensation across operational scenarios.
Magnetic navigation integration - Develop and refine sensor fusion algorithms that combine magnetometer data with IMU outputs, addressing magnetic anomaly mapping, hard/soft iron calibration, and geomagnetic model integration.
Hybrid systems architecture - Evaluate and iterate on hybrid navigation architectures (loosely-coupled, tightly-coupled, deeply-coupled), defining data flow, aiding source hierarchies, and fault-detection logic for robust multi-sensor operation.
Performance benchmarking & analysis - Define and run systematic benchmark suites against reference trajectories and truth data, tracking key metrics
Field testing & real-world validation - Plan and execute field trials across representative environments, capturing sensor logs, post-processing data, and closing the loop between lab simulation and real-world edge cases to drive algorithm refinement.
Rapid algorithm iteration - Prototype novel approaches quickly in simulation, apply structured tuning methodologies, and move promising candidates through hardware-in-the-loop testing with short iteration cycles from concept to validated result.
Critical system-level problem solving - Diagnose ambiguous failure modes across the full signal chain — from raw sensor data to navigation output — proposing unconventional solutions where standard approaches fall short and documenting findings to build team knowledge.