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We are developing autonomy for off-road construction vehicles, including autonomous articulated dump trucks (ADTs). This role owns the entire data, perception ML, and analytics stack, from raw sensor ingest to optimized edge inference and fleet-level performance visibility. You will lead a small Data and CV/ML team while remaining deeply hands-on. You are accountable for how data is collected, curated, trained, deployed, measured, and improved in production. This is a founder-mode role with direct impact on autonomy performance in the field.
Job Responsibility:
Own the full perception ML stack, from data ingest and labeling strategy through training, validation, deployment, and on-vehicle inference
Lead development of segmentation and depth estimation systems for off-road autonomy
Design and ship production-grade models using Python and PyTorch
Deploy and optimize models on NVIDIA Jetson / Orin, including TensorRT optimization and runtime profiling
Ensure models meet real-time, reliability, and resource constraints required for safety-critical systems
Own the data lifecycle: collection, mining, curation, labeling, versioning, and governance
Drive active learning pipelines to surface failure cases, edge conditions, and high-impact training data
Ensure dataset coverage aligns with operational risk, environmental diversity, and product priorities
Own the definition, interpretation, and usage of autonomy performance metrics across the fleet
Leverage and evolve the existing DFA Dune fleet analytics dashboard to ensure it reflects real-world robot behavior
Partner with the data engineer responsible for analytics
Manage a small Data team (1–2 data engineers/scientists) and CV/ML team (~3 engineers)
Set technical direction, review designs and code, and maintain a high execution bar
Ensure the team understands how their work impacts real-world vehicle performance, not just offline metrics
Clearly communicate technical progress, risks, and tradeoffs to leadership and partner teams
Maintain a high signal-to-noise ratio in communication, especially around uncertainty and failure modes
Act as the point of contact for perception and data on autonomy, systems, and field operations
Define evaluation and validation strategies spanning offline metrics, simulation, and on-vehicle testing
Support investigation of field issues related to perception failures, data gaps, or model regressions
Contribute to autonomy-readiness and safety discussions regarding perception and data quality
Requirements:
7+ years of experience in relevant fields
Excellent technical communication skills, getting and providing context from/to multiple cross-functional teams
Prior experience leading small technical teams while remaining hands-on
Proven track record deploying ML models to edge platforms, specifically NVIDIA Jetson / Orin
Experience with TensorRT optimization and inference performance tuning
Deep understanding of data-centric ML and dataset quality management
Strong production experience with Python and PyTorch
Nice to have:
Background in robotics, autonomy, or safety-critical systems
Experience with perception in unstructured or off-road environments
Familiarity with multi-sensor systems (camera, lidar, radar)