Senior/Staff Systems Modeling Engineer, Electric Propulsion

Zipline•South San Francisco, CA

2d•$180,000 - $255,000•Onsite

About The Position

You will be part of Zipline's Systems Modeling Team, which develops physics-based models used to architect, optimize, and validate Zipline's aircraft hardware and operational systems. The team works across electric propulsion, batteries, thermal systems, controls, aerodynamics, electrical engineering, and fleet operations to guide key engineering and product decisions. In this technical leadership role, you will own and advance Zipline’s electric propulsion modeling capability across motors, inverters, gearboxes and propellers. You will help define the technical roadmap and make architecture, sizing, and design decisions across current and future aircraft programs. You will drive the complete lifecycle, from first-principles model development through experimental characterization, validation, software deployment, and continuous improvement using laboratory and flight data. The scope of this role extends beyond component-level physics to integrated aircraft and fleet behavior. You will connect propulsion performance, efficiency, thermal limits, acoustics, and reliability to system-level outcomes including aircraft range, payload and mission capability, energy consumption, operational throughput and cost per delivery.

Requirements

8+ years of industry experience developing and validating physics-based models for electric powertrains, aerospace systems, electric vehicles, robotics, or other complex electro-mechanical systems.
Strong heat-transfer fundamentals and experience building, parameterizing, and validating lumped-parameter thermal networks for electric machines or power electronics.
Track record of using quantitative analysis and modeling to drive engineering decisions, including design trade studies, operating limits, performance optimization, or system architecture decisions.
Demonstrated ability to design experimental characterization campaigns for parameter identification and model validation, including defining test plans, instrumentation, and measurement requirements.
Proficiency in MATLAB, Python, Julia, Rust, or similar languages, with experience developing clean, modular, object-oriented engineering code and reusable modeling frameworks.
Solid foundation in numerical methods, optimization, controls, and engineering model validation.
Excellent communication and collaboration skills, with the ability to influence cross-functional engineering decisions through quantitative analysis.
Ability and willingness to work onsite with cross-functional engineering teams. This role requires regular hands-on collaboration with hardware, testing, and flight operations.

Nice To Haves

M.S. or Ph.D. in Electrical Engineering, Mechanical Engineering, Aerospace Engineering, or a related field.
Familiarity with motor and inverter loss mechanisms and modeling approaches.
Experience using high-fidelity electromagnetic and thermal simulation tools such as ANSYS Motor-CAD, ANSYS Maxwell, JMAG, COMSOL, ANSYS Icepak, or similar.
Familiarity with production telemetry, fleet operations, or hardware test data for engineering model validation and continuous improvement.

Responsibilities

Own the electric-propulsion modeling roadmap, framework, validation standards, and deployment of validated models across aircraft programs.
Lead trade studies that inform propulsion architecture, sizing, operating limits, thermal management, control strategies, and performance optimization.
Develop and validate physics-based models of electric propulsion systems across multiple levels of fidelity, supporting aircraft simulation, hardware design, controls development, and fleet operations.
Design and lead characterization campaigns for parameter identification and model validation, defining test requirements, instrumentation, and procedures for dynamometer, thrust stand, wind tunnel, and thermal testing.
Build and own clean, modular, object-oriented modeling frameworks and engineering tools that are maintainable, reusable, and scalable across aircraft programs.
Continuously improve model fidelity by comparing predictions against laboratory testing and production flight data, identifying model deficiencies, and deploying validated improvements that increase predictive accuracy across the fleet.
Partner closely with electrical, mechanical, thermal, controls, flight test, manufacturing, and fleet operations teams to translate modeling insights into better products and engineering decisions.