Perception Engineer - Data

Forterra
Arlington, VAPosted 24 March 2026

Job Description

About Forterra Forterra is a leading provider of autonomous systems for ground-based movement in the working world. Amongst some of the earliest innovators in the field of driverless technology, Forterra is focused on building systems that protect front-line soldiers and enable civilian workers in our industrial base. Forterra is the go-to provider of ground autonomy solutions for the U.S. Department of Defense, which harnesses the technology for asymmetric warfare in critical conditions. About Forterra Forterra is a leading provider of autonomous systems for ground-based movement in the working world. Amongst some of the earliest innovators in the field of driverless technology, Forterra is focused on building systems that protect front-line soldiers and enable civilian workers in our industrial base. Forterra is the go-to provider of ground autonomy solutions for the U.S. Department of Defense, which harnesses the technology for asymmetric warfare in critical conditions. About The Role Forterra is seeking an experienced Perception Data Engineer to join our team in supporting the development of state-of-the-art deep learning algorithms for on-road and off-road applications. The ideal candidate will have a strong background in managing large quantities of visual data from a variety of sensor types, supporting the tools for developing deep learning algorithms for online real-time perception and offline data generation in multiple sensor domains, and running on hardware operating in varied environments under real-time constraints. What You'll Do Manage and maintain the Perception Data Processing database Design, deploy, and operate Metaflow pipelines Enhance and support the Deep Learning Training Pipeline dashboard Own the Perception Data Processing unit test architecture and test asset management Develop and validate data upload/download tools for third party data annotation Support AWS infrastructure and platform reliability- Build, optimize, and distribute production model debians What We're Looking For: Mission-Focused Technical Stewardship We’re looking for someone who understands the criticality of reliable data infrastructure, reproducible pipelines, and high-integrity tooling within autonomy and perception systems. You recognize that our models, datasets, and platforms must be trustworthy, maintainable, and consistently improving to support downstream mission needs. Deep Ownership of Complex Systems You have a demonstrated ability to take full responsibility for multifaceted environments, from AWS resources to Kubernetes clusters, Metaflow pipelines, dashboards, and CI/CD systems. You proactively identify problems, trace them across layers of the stack, and implement durable fixes rather than temporary workarounds. Architect’s Mindset for Data and ML Workflows You think holistically about data ingestion, annotation, anonymization, model optimization, and deployment pipelines. You understand that data quality, reproducibility, and model lineage are foundational, and design processes that uphold these principles across environments. Collaborative Partner & Cross-Functional Influencer You work effectively with engineering, infrastructure, MLOps, IT, and security partners, navigating govcloud constraints, modifying setups, coordinating dashboard access, or working with upstream owners of tooling. You can guide others through intricate workflows, mentor teammates, and build trust across organizations. Minimum Qualifications: BS in machine learning, computer vision, or robotics, or with equivalent industrial experience. 4+ years of similar academic and/or professional working experience. Excellent understanding of standard deep learning algorithms for perception. Solid understanding of machine learning training/deployment pipelines and their implementation. Experience with PyTorch or Tensorflow, ROS, Docker, Python Excellent core software engineering skills: software design, containerization, unit t ... (truncated, view full listing at source)
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