Computer Vision Engineer

• Design, train, and iterate on custom object detection models specifically tuned for retail environments, inventory tracking, and product recognition
• Fine-tune and deploy open-source vision-language models (LLaVA, Qwen-VL, InternVL, PaliGemma, etc.) for product understanding, zero-shot classification, and scene reasoning; build vision-language-action pipelines that translate visual understanding into downstream decisions
• Take state-of-the-art models and make them blazingly fast for edge deployment through quantization, pruning, and architectural optimization
• Build robust data pipelines and annotation workflows to continuously improve model performance on diverse retail scenarios
• Stay ahead of the curve on CV and VLM research, prototype new architectures, and determine what's actually production-ready versus academic noise
• Mentor engineers, establish best practices for model development, and drive technical decisions around our CV infrastructure

Senior Computer Vision Engineer

• Prototype state-of-the-art visual encoders that power Pinterest's recommender systems and internal visual language models.
• Experiment with billion-scale datasets and gain hands-on experience with large-scale GPU computing.
• Build flexible visual reasoning tools such as composed image retrieval, promptable detection/segmentation, and instruction-tuned embedding and generative models.
• Read research papers, participate in group discussions, and help brainstorm the company's overall visual generative strategy.
• Help collect relevant visual instruction training data that can be shared across multimodal representation, composed image retrieval, text-to-image generation and visual language modeling.
• Publish and share your work through conferences, paper submissions, and blog posts.
• Mentor junior researchers and research interns within the Pinterest Labs organization.

Staff Machine Learning Engineer, Computer Vision

• Staff Computer Vision Engineers are responsible for the comprehensive lifecycle of Point One’s spatial AI and visual navigation features, overseeing everything from initial camera integration and image processing to high-level architectural and algorithmic design.
• You will conceptualize and drive complex technical challenges end-to-end - from early architecture through deployment in mission-critical systems - while raising the technical bar across the team.
• Designing solutions robust to visually challenging environments, optimizing models for compute-constrained edge devices, and ensuring our algorithms stay thoroughly tested, verified, and production-ready as we scale.
• Successful translation of state-of-the-art CV and SLAM techniques into highly performant, production-grade algorithms.
• Establishing rigorous benchmarking pipelines to objectively evaluate internal algorithms against commercial OTS solutions and vendor offerings.
• Automatically generating and maintaining accurate, semantically rich maps of complex indoor environments with minimal manual intervention.
• Ensuring real-time localization and multi-agent tracking (assets, robots, people) are highly robust, minimizing latency and identity switches even in dynamic or visually degraded conditions.
• Collaborating tightly with infrastructure and UI engineers to manage data products, render maps, and track assets for end users.

Staff Computer Vision Engineer

• Design, train, and evaluate deep learning models for segmentation, landmark detection, registration, and 3D anatomical modeling
• Develop AI supporting image-guided interventions including surgical planning, navigation, and tumor ablation
• Build image processing pipelines for DICOM/NIfTI volumetric data across X-ray, CT, and MRI
• Analyze large clinical imaging datasets and improve data quality
• Design reproducible experiments and evaluate performance with appropriate statistical methods
• Write production-quality, well-tested code and collaborate with engineering on integration
• Support V&V activities for regulated medical device development

AI/ML Engineer I – Medical Image Computing

• Design and improve training pipelines to support faster, continuous model iteration
• Enable scalable experimentation across datasets, model architectures, and training strategies
• Improve reproducibility, experiment tracking, and comparison of training runs
• Build and refine workflows for: Data indexing, curation, and preprocessing
• Label quality validation and dataset management
• Data exploration for targeted labeling
• Develop evaluation and tests that reflect real-world operating conditions
• Improve synthetic data workflows to support targeted model improvement
• Train, evaluate, and iterate on computer vision models for: Detection, segmentation, classification, tracking
• Continuously explore and evaluate: new model families and research directions
• Incorporate multi-modal inputs including: Multiple image bands, Platform and sensor metadata (range, angles, telemetry, etc.)
• Optimize models for deployment on embedded GPU platforms like Nvidia Jetson
• Balance accuracy, latency, and resource constraints for real-world, real-time workflows

Senior Machine Learning Engineer, Computer Vision

• Explore our data and workflows to identify opportunities where CV/ML-based solutions can have the most impact
• Apply classical computer vision techniques — feature matching, geometric transformations, image registration — to build internal and customer-facing features
• Prepare training data, and train, fine-tune, and validate vision-based models
• Develop cross-technology solutions that combine VLMs, vision-based models, and classical CV to solve problems no single approach can
• Ship CV pipelines that can be continuously monitored and validated, with evaluation harnesses and quality metrics built in from day one

• Design, implement, and optimize computer vision algorithms for object detection, tracking, and visual recognition on UAVs and other autonomous platforms.
• Develop 3D perception capabilities, including visual odometry, depth estimation, structure from motion, and SLAM, to support real-time mapping and navigation.
• Build sensor fusion pipelines that integrate data from cameras, IMUs, GPS/GNSS, and other onboard sensors to produce accurate, low-latency situational awareness.
• Optimize algorithms for deployment on resource-constrained edge and embedded compute platforms.
• Characterize and quantify the performance of perception components through simulation, lab testing, and real-world field testing.
• Collaborate with robotics, software, and hardware teams to integrate perception components into the broader autonomy stack.
• Write clean, maintainable, and well-documented source code adhering to modern software development practices.
• Work in a cross-functional team that takes ownership of the full software lifecycle.

• 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 Engineer – Computer Vision, Autonomous Vehicles

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.

Computer Vision Engineer jobs in United States

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Senior Computer Vision Engineer

Staff Machine Learning Engineer, Computer Vision

Staff Computer Vision Engineer

AI/ML Engineer I – Medical Image Computing

Senior Machine Learning Engineer, Computer Vision

Computer Vision Engineer

Senior Computer Vision Engineer

Senior Deep Learning Engineer – Computer Vision, Autonomous Vehicles

Senior Deep Learning and Computer Vision Engineer – Autonomous Vehicles

Computer Vision Engineer

Computer Vision Engineer