Software Engineer, Robotics Tracking and Fusion

Job description

Job responsibilities

• Define and influence the direction of a small team, leveraging your subject-matter expertise in target tracking and state estimation.
• Prototype and deploy state-of-the-art algorithms for tracking, multi-sensor data fusion, and state estimation in agile, iterative development environments.
• Develop high-performance software for real-time systems, ranging from tactical implementations to simulation environments and decision support tools.
• Design and implement robust filters, estimators, and probabilistic reasoning systems that enable actionable insights from noisy, ambiguous, or incomplete sensor data.
• Analyze system performance using high-fidelity simulations, innovative modeling tools, and rigorous statistical techniques to validate the benefits of our technology.
• Drive customer success by customizing algorithms and software for mission-critical use cases, including real-time tracking and sensor fusion.
• Integrate tracking and estimation technologies into the broader software development lifecycle, from requirements definition through testing and optimization.
• Translate technical progress into clear, actionable insights for diverse stakeholders, including colleagues and end-users.

Job requirements

• Proficiency in algorithm design, software development, and statistical modeling with programming expertise in C/C++, Python, and Matlab.
• Strong knowledge of target tracking techniques, such as Kalman filters, particle filters, and multi-target tracking algorithms (e.g., JPDA, MHT, or PHD filters).
• Experience in state estimation, including Bayesian filtering, sensor fusion, and recursive estimation techniques.
• Solid understanding of applied mathematics, including linear algebra, optimization, probability, and stochastic processes.
• Knowledge of signal processing techniques for interpreting diverse sensor data (e.g., radar, lidar, EO/IR).
• Familiarity with big data pipelines, NoSQL databases, and the efficient handling of large-scale sensor data.
• Background in machine learning as applied to target tracking and recognition, including clustering, classification, and anomaly detection techniques.
• Ability to engineer robust systems for estimation theory, adaptive filtering, controls, and complex signal environments.