Path Planning Engineer

About Forterra

At Forterra, we are unleashing autonomy at scale to transform the battlefield. Our mission is to build the foundational platforms that enable an intelligent ecosystem to coordinate, adapt, and execute with speed and precision even in the uncertainty and disruption of modern conflict. In an era marked by rapid technological change and evolving threats, we design for flexibility, survivability, and operational dominance.

Forterra delivers weapons, sensors, and battlefield effects through integrated autonomous networks reaching operational areas faster, safer, and without placing human lives  systems operate with distributed control, dynamic routing, and real-time responsiveness, enabling sustained advantage across complex mission environments.

Forterra is a fast-growing company at the early stages of growth. We hire talented people with diverse backgrounds who are ready to help build a technology ecosystem that supports our defense and commercial partners.

This role is focused on bridging planning algorithms with real-world vehicle systems, ensuring that autonomy performs reliably under real-time, on-vehicle constraints. You will be part of a team tackling planning problems across diverse domains such as complex, unstructured off-road terrain, coordinated formation control, and semi-structured environments like logistics yards. Platforms range from small robotic vehicles to full-scale trucks.

In addition to algorithm development, this role requires deep involvement in on-vehicle integration, system bring-up, debugging, and performance validation in field environments. Members of our Behavior Generation team must be resourceful, hands‑on, and capable of owning problems end‑to‑end—from design through deployment on physical systems.

• Develop real-time (deterministic, randomized, and optimization-based) path and motion planning algorithms for various vehicle types (Ackermann, skid‑steered, wheeled, tracked) using C++ in a Linux environment
• Integrate planning algorithms with on‑vehicle systems, including perception, localization, controls, and platform interfaces
• Deploy, test, and debug autonomy software directly on vehicles, addressing real‑time constraints, system latency, and hardware limitations
• Design and implement messaging for synchronization, logging and debugging across distributed systems
• Own end‑to‑end validation: simulation → HIL → on‑vehicle testing, ensuring consistency and performance across environments
• Diagnose and resolve issues observed in field testing, including edge cases arising from sensing, actuation, and environment variability
• Work closely with cross‑functional teams (Perception, Controls, Platform, Systems Engineering) to ensure robust and reliable vehicle behavior
• Travel up to 20% to support on‑site vehicle integration and testing

• BS, MS, or PhD in Robotics, Applied Mathematics, Mechanical Engineering, Computer Science, or related field
• Strong background in path planning, motion planning, or related autonomy domains
• Strong programming skills in C++
• Solid software engineering fundamentals: system design, unit/integration testing, debugging
• Experience deploying or integrating software on physical robotic or automotive platforms
• Ability to deliver production‑quality software in a continuously integrated environment
• Demonstrates clean, maintainable code and documentation practices
• Strong problem‑solving skills with a proactive, hands‑on approach

• Prior experience with unmanned ground vehicles operating in outdoor environments
• Experience with on‑vehicle debugging, telemetry analysis, and real‑time system profiling
• Familiarity with robotics middleware (e.g., ROS/ROS2 or similar frameworks)
• Experience working across perception–planning–controls interfaces
• Expertise with GPU or ML toolkits such as CUDA, PyTorch, Tensor Flow, and/or TensorRT

$150,000—$180,000

The salary range for this role is an estimate and is based on a wide variety of compensation factors. The salary offered to candidates will vary based on a variety of factors including (but not limited to) relevant work experience, education, specialized…