Senior Controls Engineer

Parallel Systems•Los Angeles, CA

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 involves modeling, simulating, and designing software that controls vehicle electrical and mechanical systems, and verifying the software in simulation before deployment to hardware. The position calls for strong control systems theory, extensive simulation-based controller development and verification experience, and a track record of delivering high-quality real-time control software. The engineer will develop plant models for vehicle dynamics, powertrains, and braking systems; design and validate control algorithms; and build simulation infrastructure used to verify control software ahead of vehicle testing. The role also uses modeling and simulation to establish sensor and actuator requirements, correlates models against test and field data, and supports system integration and vehicle testing. The engineer will help ensure control software is reliable, safe, and performs well. Within the first 30 days, the successful candidate is expected to define requirements and architecture for the platooning simulation suite, run existing code, and create a feature roadmap. Within 60 days, they are expected to implement a simulation MVP, run test cases, assess baseline platooning-model assumptions, and identify control-stack improvements. Within 90 days, they are expected to use HOOTL simulation to drive control-stack performance upgrades, implement onboard features for real-world testing, and run a 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, including 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.
Proficiency 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, such as string stability or cooperative adaptive cruise control (CACC).
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.
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 ahead of 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 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 real test cases, evaluate platooning-model assumptions, and identify control-stack improvements.
Use HOOTL simulation to drive control-stack performance upgrades, implement onboard features for real-world testing, and run vehicle tests to validate model outputs.