Computer Vision Engineer

• Stay up to date with the latest research and innovations in deep learning, implement and experiment with new ideas.
• Foster collaborative partnerships with our researchers and engineers, transforming innovative research into robust, industrial-strength machine learning models.
• Taking approaches from initial evaluation and experimentation all the way to shipping.
• Defining and collecting training datasets.
• Building training pipelines and real-time inference run-times (PyTorch, TensorFlow, TensorRT, Python, C++).

Senior Deep Learning and Computer Vision Engineer – Autonomous Vehicles

• Own the development and deployment of advanced CV models — object detection, multi-object tracking, camera calibration, and semantic segmentation — for player tracking across multiple sports
• Build and scale high-performance pipelines for video and image data ingestion, processing, and analysis that hold up under the demands of professional and collegiate programs
• Contribute across the full CV/ML lifecycle, from data gathering, model development and training through production deployment, monitoring, and iterative refinement
• Drive R&D initiatives including 3D body pose tracking, real-time analysis systems, and integration with LLM/NLP research — with real latitude to explore within the scope of each project
• Collaborate closely with Software Engineering, Product, and Data Ops to ensure your work is understood, integrated, and used correctly downstream

• Build and train CV models for sports video: player/ball detection, multi-object tracking, pose/keypoints, event/action recognition, identity association (re-ID).
• Own the experimentation loop: hypotheses → ablations → error analysis → measurable improvements.
• Design and maintain evaluation: task-appropriate metrics (e.g., MOT metrics, keypoint accuracy, event precision/recall), dataset slices, and failure taxonomy.
• Improve data efficiency: augmentations, sampling strategies, handling label noise, weak/self-supervision where helpful.
• Prototype and iterate on modern architectures (e.g., transformer-based detection/tracking, temporal models, multi-task setups).
• Collaborate on dataset + labeling design: formats, schemas, tooling, versioning.
• Help productionize models: packaging, batch/stream inference patterns, throughput/latency tradeoffs, robustness checks.
• Add lightweight quality gates: reproducibility, automated eval, regression detection.

• Own camera and multi-sensor calibration across our capture rigs, including intrinsics, extrinsics, and time synchronization
• Build, evaluate, and improve SLAM, VIO, and mapping pipelines that recover aligned 6-DoF trajectories from real-world captures
• Train and/or fine-tune models for pose estimation and semantic understanding of multi-modal data
• Diagnose and fix the failures that actually show up in the field – drift, calibration drift, sensor misalignment, degraded tracking, weak reconstructions, noisy data
• Define the ground-truth and benchmarking methodology we use to know whether the spatial layer is actually getting better
• Decide where we need custom perception work versus where off-the-shelf components are good enough
• Work closely with the rest of engineering and with Trace Labs (our applied research arm) to feed reliable spatial outputs into downstream annotation, evaluation, and product workflows

Senior Computer Vision Engineer

• Leverage extensive programming and machine-learning expertise to design, architect, and optimize AI software and hardware that utilizes visual data
• Lead the integration and deployment of computer vision solutions into satellite systems
• Conduct rigorous testing and validation of computer vision systems
• Develop and optimize sophisticated AI software and hardware for satellite applications
• Maintain, enhance, and refactor existing computer vision systems and algorithms
• Document code, processes, and methodologies comprehensively for future reference and team collaboration
• Mentor and guide junior and mid-level engineers
• Stay abreast of the latest advancements in computer vision and machine learning technologies
• Collaborate with product managers and stakeholders to define project requirements, deliverables, and timelines

Computer Vision Engineer III – Optics Engineer

• System Architecture: Design, develop, and maintain production-grade computer vision systems that power Niantic Spatial’s VPS (Visual Positioning System) and 3D mapping pipelines.
• Algorithmic Innovation: Refine and scale algorithms for our VPS system including, SfM (Structure from Motion), efficient feature and descriptor extraction to have the best possible accuracy and recall..Moving algorithms from research concepts to hardened production code that can scale for our map of the world.
• Performance Engineering: Optimize complex ML and CV code for maximum efficiency in cloud and mobile environments, ensuring low latency and high-performance execution on GPU/CPU.
• Benchmarking & Evaluation: Create and own the tools and frameworks used to evaluate the quality of our spatial grounding and 3D maps against ground-truth data.
• Technical Leadership: Lead technical design reviews, mentor junior engineers, and serve as a team anchor for resolving complex technical disagreements within the mapping stack.
• Cross-Functional Delivery: Collaborate with Product, Research, and Operations teams to ensure that state-of-the-art computer vision solutions translate into delightful user experiences.

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Senior Deep Learning and Computer Vision Engineer – Autonomous Vehicles

Computer Vision Engineer

Computer Vision Engineer

Senior Computer Vision Engineer

Computer Vision Engineer III – Optics Engineer

Senior Computer Vision Engineer