Senior Controls Engineer

Parallel Systems•Los Angeles, CA

5d•$175,000 - $215,000

About The Position

Parallel Systems is seeking a Senior Controls Engineer to join its engineering team and develop control systems for autonomous battery-electric rail vehicles. The role focuses on modeling, simulating, and designing software that controls vehicle electrical and mechanical systems, and verifying that software in simulation before deployment to hardware. The ideal candidate has a strong foundation in control systems theory, extensive simulation experience for developing and verifying controllers, and a record of delivering high-quality real-time control software. The engineer will develop plant models for vehicle dynamics, powertrain, and braking systems; design and validate control algorithms; and build and maintain simulation infrastructure, including MIL, HOOTL, and HITL environments. The role also includes deriving sensor and actuator requirements through modeling, comparing simulation results with test and field data, and supporting system integration and vehicle testing. The position emphasizes reliability, safety, and performance in control software. Within the first 30 days, the engineer is expected to define requirements and architecture for the platooning simulation suite, run existing code, and create a feature roadmap. Within 60 days, the engineer is expected to implement a simulation MVP, run real test cases, assess the platooning model’s baseline assumptions, and identify controls-stack improvements. Within 90 days, the engineer is expected to use HOOTL simulation to guide controls-stack upgrades, implement onboard features for real-world testing, and run a test campaign using Parallel vehicles to validate model outputs and ground the software stack.

Requirements

Bachelor’s or Master’s degree in Electrical Engineering, Mechanical Engineering, Software Engineering, Mathematics, or a related field.
At least 5 years of experience developing control software for physical systems, with substantial time spent in modeling and simulation.
Strong understanding of control theory, including PID, state-space, observers, system dynamics, and system identification.
Proficiency in C/C++, Python, or similar languages used in simulation and embedded systems development.
Proficiency with modeling and simulation tools such as MATLAB/Simulink, Isaac Sim, or equivalent Python-based frameworks, and with MIL, SIL, and HIL testing workflows.

Nice To Haves

Experience building simulation infrastructure, digital twins, or automated simulation-based regression testing for control software.
Experience with distributed or multi-agent control, including string stability, cooperative adaptive cruise control (CACC), or similar.
Experience with state estimation, including Kalman filtering and sensor fusion, and system identification from field data.
Experience with robust control or hybrid systems combining continuous dynamics with discrete events such as contact and impact.
Experience with NVIDIA Isaac Sim, Omniverse, or similar simulators such as Gazebo, MuJoCo, or Drake.
Familiarity with embedded platforms such as ARM Cortex, DSPs, or RTOS-based systems.
Exposure to the automotive, aerospace, or robotics industries.
Experience developing with Rust, especially on embedded platforms.
Experience modeling and simulating vehicle dynamics, braking systems, electric powertrains, or sensor fusion.
Prior ownership of a controls project from modeling and simulation through implementation, testing, and deployment.

Responsibilities

Develop and maintain plant models of vehicle dynamics, powertrain, and braking systems, and use them to analyze, simulate, and tune controllers using tools such as MATLAB/Simulink or Python.
Design and implement control algorithms for vehicle systems, validating them in simulation before deployment to embedded targets.
Build and own simulation infrastructure, including model-in-the-loop (MIL), hardware-out-of-the-loop (HOOTL), and hardware-in-the-loop (HITL) environments, to verify control software before vehicle testing.
Use modeling and simulation to derive sensor and actuator requirements that meet system performance needs.
Correlate simulation results against test and field data to validate and improve model fidelity.
Support system integration and vehicle testing, using simulation to reproduce and debug issues observed on hardware.
Ensure high reliability, safety, and performance in all control software implementations.
Define requirements and architecture for the platooning simulation suite, run existing code, and develop a feature roadmap.
Implement a simulation MVP, run test cases, evaluate platooning-model assumptions, and identify controls-stack performance improvements.
Use HOOTL simulation to drive controls-stack upgrades, implement onboard features for real-world testing, and conduct vehicle testing to validate model outputs.