Computer Vision Engineer

• Design, develop, implement, and integrate advanced Computer Vision and Artificial Intelligence features
• Analyze and optimize vision and AI pipelines for real-time performance on CPU, GPU, NPU, and other hardware accelerators
• Architect efficient software stacks for complex CV/AI workflows with minimal latency and high power efficiency
• Collaborate with product, research, and engineering teams to integrate AI models and vision-based solutions into the final product
• Rapidly prototype and iterate on algorithms for perception and inference problems
• Develop embedded systems for high-performance applications

Senior Software Engineer – Computer Vision

• Turn research ideas and prototypes into reliable computer-vision and 3D reconstruction pipeline components and services
• Ensure reconstructed environments and spatial artifacts are geometrically coherent, correctly scaled and aligned, and usable by downstream simulation and embodied AI systems
• Diagnose failures caused by capture quality, scene complexity, scale, calibration, coordinate conventions, and other implicit assumptions
• Profile and optimize GPU and distributed workloads, reduce unnecessary reruns, and help establish the economics of running reconstruction at scale
• Create datasets, regression tests, quality gates, and benchmarking tools
• Bring production and customer constraints into research planning and prioritize advances for embodied AI
• Partner with Embodied AI product and engineering teams to understand customer requirements and deliver capabilities for training, evaluation, and deployment workflows
• Evolve representations, tooling, and operational systems for moving reconstructed environments into customer applications
• Work across research, engineering, product, and customer teams to turn advances in 3D reconstruction and spatial perception into production capabilities

Senior Computer Vision Engineer, Embodied AI

• Collaborate with product managers to translate real-world challenges into practical CV/ML solutions that create a measurable impact
• Design and develop computer vision and vision-language solutions using both classical techniques and modern AI approaches
• Optimize, deploy, and support models across cloud and edge environments, ensuring efficiency and robustness
• Establish evaluation frameworks for CV and VLM models, including real-world performance metrics
• Monitor deployed systems, debug issues, and refine models with new data or improved architectures
• Stay up to date with emerging research in computer vision; apply new techniques to improve vision products

AI Computer Vision Engineer

• Own the modeling approach for visual inspection, including architecture selection, training strategy, and accuracy, latency, and false-reject targets
• Define and maintain the evaluation methodology and regression suite that gates model releases
• Version, sample, and audit labeled data
• Optimize models for constrained edge targets through quantization, runtime selection, and throughput tuning
• Own drift detection, retraining triggers, and production model monitoring across deployed customer installations
• Partner with customers and application engineers on feasibility, data collection strategy, and acceptance criteria
• Set the technical bar for the ML discipline through design review, mentoring, and written architecture guidance
• Apply agents and evals to the ML workflow, including dataset triage, failure-mode summarization, and experiment scaffolding
• Measure whether AI workflow improvements actually improve cycle time
• Report to the Director, Software Engineering

Machine Learning Engineer – Computer Vision

• Develop and improve object-detection and multi-object-tracking models for vehicles, pedestrians, and other road users in challenging real-world conditions
• Own data strategy for model quality, including sampling, labeling, dataset versioning, hard-negative mining, class imbalance, edge cases, and deployed-system feedback
• Design rigorous experiments and ablations and distinguish genuine improvements from overfitting, leakage, noisy labels, or gains that do not survive field deployment
• Build and evolve reproducible training pipelines with experiment tracking, configuration management, artifact lineage, model registries, metric dashboards, and automated hyperparameter search
• Define product-relevant evaluation covering precision/recall trade-offs, class and scenario slices, calibration, and tracking quality
• Partner with embedded engineers to optimize models for edge deployment while balancing accuracy, latency, memory, and power constraints
• Set technical direction, lead design reviews, mentor engineers, and raise standards for ML rigor, reproducibility, and production readiness