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California, United States
Onsite
We are a closely-knit team aspiring to change the world through disruptive technology. We are innovators. We are tinkerers. We are problem-solvers. And we have a fair amount of magic dust up our sleeves. We have a plan for fleet-level deployment of autonomous vehicles, and we are looking for the best-of-the-best to join us in making this a reality.
Based in the U.S. and Asia, Venti Technologies is the leader in safe-speed autonomous logistics systems, developing the future of goods transportation. Using rigorous mathematics, deep learning, and theoretically-grounded algorithms, Venti has a proprietary collection of autonomy technologies including a suite of powerful logistics algorithms. Venti’s proven value proposition of saving costs, increasing vehicle utilization, and improving safety is recognized by customers and driving growth.
Launched in 2018, Venti brings together an unsurpassed team internationally. The company has autonomous systems deployed in Asia for industrial and logistics sites and a growing pipeline. Venti has offices in Cambridge (Massachusetts, USA), Suzhou (China), and Singapore – our Asian headquarters.
About the Role
We are looking for an Onsite Validation Engineer to help bring up new autonomous vehicles, ensuring safety and reliability from the ground up. In this role, you will validate critical vehicle systems—including steering, braking, and throttle—by performing thorough static and dynamic checks.
This position bridges software and hardware. You will develop automated testing scripts for vehicle bring-up, build foolproof tools for system calibration, and validate vehicles post-maintenance. Additionally, you will develop control testing scripts, evaluate vehicle behavior in real-world environments, analyze test data, and collaborate with cross-functional teams to continuously improve system performance.
Execute Vehicle Bring-Up: Lead the initial bring-up of new vehicles, validating the steering, brake, and throttle systems through comprehensive static and dynamic checks.
Develop Automation Tools: Write automated testing scripts for vehicle bring-up and build foolproof tools to calibrate brakes and steering, as well as to validate vehicles after routine maintenance.
Control Software Development: Contribute to the design, development, and maintenance of software for vehicle motion control, focusing on steering and speed control.
System Modeling: Build mathematical models of vehicle behavior by characterizing steering, braking, throttle, actuator response, and system delays.
Optimize Performance: Improve controller performance through rigorous testing, tuning, and calibration across different vehicle platforms and operating conditions.
Data Analysis & Diagnostics: Collect and analyze vehicle test data to diagnose issues, validate controller behavior, and identify opportunities for continuous improvement.
Ensure Safety: Design and maintain safety features within the control system, including fault detection, command validation, timeout monitoring, and safe recovery strategies
Bachelor's or Master's degree in Robotics, Control Engineering, Computer Science, Mechanical Engineering, Electrical Engineering, or a related discipline.
Around 3 years of experience in robotics, control systems, autonomous vehicles, or a similar technical field through industry, research, internships, or academic projects.
Strong understanding of feedback control principles, vehicle dynamics, and controller evaluation.
Experience with system modeling, system identification, or dynamic system analysis.
Familiarity with electromechanical, hydraulic, or pneumatic actuation systems.
Proficiency in modern C++ development on Linux.
Strong analytical thinking, troubleshooting ability, and a willingness to learn new technologies.
Experience with ROS or ROS 2.
Experience integrating software with drive-by-wire or embedded vehicle control systems.
Hands-on experience deploying robotic or autonomous systems outside of simulation in real-world environments.
Knowledge of functional safety practices, risk analysis, or control-related failure analysis.
Understanding of the overall autonomous driving software stack and how control interacts with perception, localization, and planning.