Blue River Technology

Machine Learning Engineer

• Research and develop image-processing and computer-vision algorithms for 2D and 3D scene segmentation.
• Develop effective experiments and methods to detect and characterize model performance issues in long-tail conditions.
• Exploring Deep Learning models and/or frameworks to push the performance frontiers of our current system, including exploring methods in the active learning space.

Machine Learning Intern, Autonomy

• Build production models, designing, training, and deploying production-grade deep learning models for critical perception tasks.
• Advanced Edge optimization including fine-tuning models for real-time inference on embedded NVIDIA GPU platforms.
• Own model development from rapid prototyping through to validation and final deployment.
• Collaborate closely with robotics team to integrate perception outputs into the robotics stack.
• Analyze performance on mowing site data, identify failure cases, and continuously improve models.
• Develop strategies for using real and synthetic data to train models and validate performance.

Senior CVML Engineer, Autonomy

• Execute reproducible experiments on our large data catalog to further the understanding of model performance.
• Implement and evaluate novel CVML techniques, ranging from CNNs to large Transformer-based Foundation models, to improve our products.
• Execute and improve existing rigorous model evaluation pipelines.
• Collaborate on debugging and troubleshooting issues in ML models.
• Lean into the responsible use of AI for software development.

CVML Engineer, See and Spray

Data Engineer

• Build and maintain production and engineering pipelines for the data lifecycle, including Data Upload, Data Processing, Monitoring, and Data Visualization.
• Design, develop, and test full-stack applications that help analyze vector and raster data.
• Implement smart data offboarding solutions for Cloud and Edge compute environments.
• Document code, APIs, and system architecture to ensure knowledge sharing.
• Own developing a continuous delivery pipeline for all ingest-related software in Kubeflow/Airflow and Databricks.
• Collaborate with cross-functional partners such as the Product, QA, CVML, and Robotics teams.
• Build and maintain Data Collection tools.

Software Data Engineer

Robotics

• Serve as the outcome and domain owner for a portfolio of technical programs; be prepared to bootstrap ambitious, ambiguous, large-scale strategic initiatives into clear, structured execution paths.
• Weave complex, cross-organizational interdependencies into a singular, harmonious program strategy that unifies diverse engineering groups.
• Evangelize the program's strategy across the core platform engineering teams and the robotics programs that use our foundational tooling.
• Replace administrative status updates with an authoritative, bottom-up framework that clearly articulates milestones, sequences, and critical paths to executive leadership.
• Act as a high-velocity roadblock smasher, using your program tooling to identify engineering bottlenecks and re-chart course as necessary to guarantee high-quality, on-time program delivery.
• Build the connective tissue between cross-functional groups, ensuring deep understanding, operational symmetry, and goal alignment across teams.
• Shape and contribute deeply to strategic roadmaps for developing our robotics platform.
• Work cross-functionally to develop best practices and development processes in a dynamic environment, driving impact through the deployment of key initiatives and garnering adoption of those processes.

Senior Technical Program Manager, ML & Robotics Platform

Infrastructure Engineer

• Design and own our data pipeline, from ingestion to training to search to reinforcement-learning correction data, and hopefully one day to simulation.
• Grow the scale of our training pipeline from 10’s of hours of data to 1000’s of hours of data.
• Design extensible software to work across many vehicle platforms and jobs.
• Evaluate new models and model architectures.
• Work closely with ML engineers and robotics engineers, collaborate to solve cross-disciplinary problems, and improve performance.
• Make our project and customers successful.
• Be an ambassador for our needs and help steer us towards an E2E-first organization.

Senior ML Infrastructure Engineer, E2E Autonomy

Technical Program Manager

• Manages end-to-end programs of moderate to high complexity, spanning multiple teams and functions.
• Develops detailed plans, optimizes workflows, and ensures deliverables align with strategic objectives.
• Proactively identifies, analyzes, and manages program risks, issues, and dependencies, demonstrating good judgment in developing and deploying mitigation strategies.
• Drives consensus and influences without direct authority among cross-functional stakeholders to achieve program objectives.
• Facilitates effective communication between technical and non-technical teams, providing clear program status and communicating challenges to stakeholders.
• Tracks and measures project outcomes, identifying opportunities for continuous improvement and process optimization.
• Removes impediments to enable engineering development and holds team members accountable for deliverables.
• Proactively follow up with team members to ensure on-time delivery of their committed outputs.

Technical Program Manager, E2E Programs

Product Manager

• Own strategy, roadmap, and outcomes for one or more Platform offerings or cross-cutting capabilities
• Build tight feedback loops with program teams and engineers to understand real constraints in the field
• Translate problems into crisp requirements, success metrics, and prioritized backlogs; make explicit tradeoffs across speed, reuse, reliability, and customization
• Partner with engineering and design to ship iteratively—APIs, tooling, workflows, and user experiences that teams adopt and keep using
• Drive onboarding, documentation, enablement, and rollout plans; measure adoption and unblock teams during integration
• Set the bar for production readiness (reliability, observability, supportability, and backward compatibility)
• Coordinate dependencies across Platform and program teams; reduce duplication and converge on shared building blocks where it matters
• Define and track product metrics and use them to drive priorities and investment decisions
• Keep stakeholders aligned through clear narratives, status updates, decisions, and risk assessments, especially when priorities conflict or timelines shift.