Data & CVML Manager, Autonomy

Blue River Technology•Santa Clara, CA

5d•$192,000 - $287,000•Onsite

About The Position

We’re Blue River, a team of innovators driven to create intelligent machinery that solves monumental problems for our customers. We empower our customers – farmers, construction crews, and foresters - to implement safer and more sustainable solutions, driving increased profitability with less reliance on scarce labor. We believe that focusing on the small stuff – pixel-by-pixel and task-by-task - leads to big gains. Blue River Technology aligns with John Deere’s vision to "innovate on behalf of humanity" by quickly identifying and solving high-value, high-uncertainty challenges in AI, machine learning, computer vision, and robotics. BRT acts as a research and development flywheel, building not only new products but also new platforms that reliably create value for both Deere and its customers. From fully autonomous machines to highly precise farming equipment, BRT and Deere are partnering to create technical breakthroughs in industries like agriculture and construction. Our people are at the heart of what we do. Through cross-disciplinary collaboration, this mission-driven team is eager to define the new frontier of robotics. We are always asking hard questions, rapidly iterating, and getting our boots in the field to figure it out. We won’t give up until we’ve made a tangible and positive impact on the planet! Blue River Technology is based in Santa Clara, CA. Summary 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.

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 Haves

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).

Responsibilities

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 to: Ensure metrics are accurate, actionable, and trusted Tie perception and ML performance directly to in-field autonomy outcomes.
Use analytics to identify regressions, guide data collection, prioritize model improvements, and inform leadership.
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.