Top Computer Vision Jobs in NYC, NY

Design, develop, deploy, and support scalable AI systems, including foundation models, LLM inference, agentic workflows, vector search, guardrails, evaluation, and observability. Optimize model training and inference for cost, latency, throughput, and hardware utilization. Own complex AI architecture, reliability objectives, governance, security, and compliance while collaborating across engineering, research, product, and program teams. Lead technical reviews, shape foundational AI roadmaps, and mentor senior technical staff.

The Senior Staff Machine Learning Scientist will lead research initiatives in computer vision and multimodal understanding, develop novel models, and translate research into practical applications while mentoring team members.

Develop, train, and optimize computer vision and ML models (particularly video-based safety) using petabyte-scale camera and sensor data. Deploy and optimize models for backend and edge inference, collaborate with cross-functional teams, and translate customer needs into scalable ML/CV solutions.

Owns computer vision systems end to end, from roadside camera configuration and image quality through model development, edge inference, production deployment, monitoring, and customer outcomes. Builds detection, tracking, OCR, attribute extraction, embedding, and re-identification systems; optimizes pipelines for constrained edge hardware; investigates field failures; improves datasets and evaluation frameworks; and collaborates with engineering and field teams to deliver reliable roadside intelligence at scale.

Develop proprietary foundation models for 3D hand tracking, articulated pose estimation, mesh recovery, and hand-object interaction. Train large-scale vision architectures across multi-GPU clusters using massive egocentric manipulation datasets. Innovate on biomechanically constrained losses, temporal modeling, contact and physics-aware tracking, tactile-visual fusion, and robotics perception pipelines. Rapidly prototype solutions for emerging hardware and embodied AI challenges.

Build backend systems that translate computer vision and machine learning outputs into interactive product experiences. Responsibilities include designing perception-to-product data contracts, shipping AI-generated geometry to a canvas, balancing accuracy with user experience, closing human correction loops, and bridging AI/ML and product engineering teams. The role requires Python and TypeScript, familiarity with PyTorch and model postprocessing, data contract versioning, and experience deploying computer vision or ML outputs in user-facing products.

Build and own real-time computer vision systems for warehouse environments, including multi-camera video capture, object detection, tracking, camera calibration, bird’s-eye-view projection, and GPU edge deployment. Improve accuracy, latency, and reliability through model fine-tuning, evaluation tooling, optimization, and production diagnostics. The role also includes adjacent backend, deployment, observability, and administrative systems work.

Develop computer vision and vision-language models for image and video understanding in restaurant environments. Build datasets, training strategies, evaluations, and benchmarks; diagnose model failures; and deploy efficient, reliable inference pipelines. The role combines research and engineering across scene understanding, object tracking, activity recognition, temporal reasoning, visual grounding, and multimodal models, while partnering with product and infrastructure teams to bring research into production.

Conduct research on world modeling and 3D geometry, developing self-supervised learning methods, video-based world-dynamics architectures, scalable data pipelines, evaluation frameworks, and model-based planning algorithms. The role requires independent experimentation and analysis in machine learning, large-scale training, and GPU or TPU environments, with preferred expertise in 3D/4D reconstruction, SLAM, SfM, depth and pose estimation, point tracking, rendering, simulation, and deep learning frameworks.

Conduct research and engineering to improve Claude's visual and spatial capabilities across pretraining, SL, and RL. Run experiments on architectures and data strategies, build agentic tools and evaluation benchmarks, develop data ingestion pipelines, and partner with product to deploy vision improvements for real-world API use cases.