System Identification Software Developer

Math Works has a hybrid work model that enables staff members to split their time between office and home. The hybrid model provides the advantage of having both in-person time with colleagues and flexible at-home life optimizations. Learn More:

Would you like to develop algorithms and AI technologies that enable engineers to learn dynamic system models directly from data?

Join a team developing MATLAB® and Simulink® capabilities for system identification, dynamic system learning, and online estimation. Our work combines dynamic systems, machine learning, estimation, signal processing, and control to create algorithms and workflows used by engineers to model complex systems from measured data.

The team is also developing new AI-enabled engineering capabilities, including specialized AI skillsets and agents that make advanced modeling and estimation techniques easier to apply to real-world engineering problems.

Math Works nurtures growth, appreciates inclusivity, encourages initiative, values teamwork, shares success, and rewards excellence.

You will develop algorithms and software spanning MATLAB, Simulink, engineering apps, and emerging AI agentic tools. You will:

• Develop core algorithms for system identification and online parameter and state estimation

• Create machine learning and AI methods for learning linear and nonlinear dynamic system models

• Build MATLAB and Simulink workflows, apps, AI skillsets, and specialized AI agents

• Implement and test production-quality software, including support for Simulink and Simulink Coder workflows

• Create examples and workflows demonstrating new capabilities on representative engineering problems

• Plan and communicate development activities in two-week sprint cycles while contributing from research and discovery through design, implementation, testing, and product delivery

• A bachelor's degree and 3 years of professional work experience (or a master's degree, or equivalent experience) is required.

• Knowledge of linear and nonlinear dynamic systems

• Experience with system identification, parameter estimation, machine learning or deep learning

• Knowledge of Kalman filtering, state estimation, or recursive estimation methods

• Ability to present technical designs, results, and development status to colleagues