Design and scale the autonomy stack for heavy-construction robots by integrating perception, planning, and control. Build simulation environments, improve perception pipelines and sensor calibration, enable learned-model deployment, develop data collection and validation workflows, diagnose real-world failures, and deploy robust manipulation systems for field use.
Company Description
Role Description
Key Responsibilities
Qualifications
Heavy infrastructure cannot be built fast enough to fulfill the promises of American reindustrialization. With labor availability tightening and the workforce aging, construction faces a multi-trillion-dollar execution gap over the next decade. Backed by Founders Fund and others, the team is developing robots for outdoor, high-payload job site work with a focus on immediate deployment and autonomy that delivers value immediately. We’re partnering with the largest heavy construction firms to put robots on sites immediately, building hardware, data, and commercial moats to bring end-to-end autonomy to construction.
We are looking for a Founding Autonomy Engineer to help design and scale the autonomy stack for Strata’s robotic platform. This role focuses on building a robust and extensible autonomy system, shaping how perception, planning, and control components integrate and operate together. You will work across system design and real-world deployment to identify failure modes, improve observability, and ensure the system remains reliable and scalable. You will help design and build systems that allow the autonomy stack to incorporate learned models and leverage simulation for faster iteration and more complex tasks. This role requires experience solving complex problems and building systems that are reliable and scalable in real-world environments.
- Evolve and improve the autonomy system, shaping how perception, planning, and control components integrate and operate together.
- Make system design decisions that ensure the autonomy stack remains robust and scalable, and can reliably incorporate learned models into real-world deployments.
- Identify and resolve system-level issues in real-world deployments, including failures across sensing, state estimation, planning, and control.
- Define and improve interfaces and data flow across perception, planning, and control to ensure clean integration and maintainability.
- Develop and maintain simulation environments and workflows (MuJoCo, Gazebo, Isaac Sim, or similar) to support testing, validation, and development of new capabilities.
- Build systems to support data collection, evaluation, and iteration for learned models deployed in the autonomy stack.
- Improve perception pipelines, including sensor calibration, synchronization, and integration of camera and point cloud data to support more advanced autonomy capabilities.
- Define and improve testing and validation workflows for autonomy behaviors in both simulation and real-world environments.
- Improve system robustness through better handling of noise, uncertainty, and real-world variability.
- Build and deploy manipulation systems that enable robots to reliably move materials in real-world environments.
- Bachelor’s, Master’s, or PhD in Robotics, Computer Science, or a related technical field.
- Strong hands-on experience building manipulation systems for physical robots, not just simulation environments.
- Proficiency in Python and C++ for robotics software development.
- Experience with ROS2 and common robotics tooling for integration, visualization, and debugging.
- Familiarity with manipulation and perception frameworks such as MoveIt, OpenCV, PCL, and related tools.
- Experience integrating and calibrating cameras or depth sensors for robotics applications.
- Strong understanding of motion planning, kinematics, and control.
- Ability to debug full-stack manipulation problems across sensing, estimation, planning, controls, and hardware behavior.
- Comfort working directly on robotic systems in fast iteration loops involving bench testing, system bring-up, and field testing.
- Experience with logging, replay, and debugging workflows using tools such as RViz and rosbag.
- Strong engineering judgment and the ability to choose practical, robust solutions that work under real deployment constraints.
The base pay range for this role is $100,000 – $180,000 per year.
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