Lead Computer Vision Engineer

Remote, USA • Full-time • Posted 2026-04-04

We are looking for a Technical Lead to architect and deploy end-to-end Computer Vision solutions. You will lead a team of engineers to translate abstract business needs into high-performance, real-time vision pipelines that run on the Edge (NVIDIA Jetson/GPUs) and the Cloud.

Technical Leadership: Lead the end-to‑end execution of Vision AI projects from algorithm selection and prototyping to production deployment. Mentor junior engineers and set high standards for code quality and fault tolerance.
Architect Real-Time Pipelines: Design low‑latency camera stream processing pipelines for Object Detection, Tracking, OCR, and Behavior Analysis using state‑of‑the‑art architectures (Transformers, YOLO, etc.).
GenAI Integration: Push the boundaries by integrating Generative AI (Vision Language Models / VLMs) into our industrial workflows to provide deeper intelligence.
Customer Collaboration: Bridge the gap between "Research" and "Reality." Translate client business requirements into techno‑analytic problems and deliver disruptive insights in reasonable timeframes.
Infrastructure Collaboration: Work closely with the DevOps team to ensure seamless containerization and orchestration (Docker/Kubernetes) of your models.

Core CV & ML: Deep mastery of Python and the Computer Vision ecosystem (PyTorch, OpenCV, NumPy). Strong grasp of Machine Learning / Deep Learning fundamentals.
Video Analytics Mastery: Proven experience processing live RTSP/Camera feeds at high FPS. You understand the difference between running a model on a static image vs. a continuous stream.
Inference Optimization: You don't just train models; you deploy them. Experience with NVIDIA TensorRT, DeepStream, or Triton Inference Server is highly valued.
Production Engineering: Ability to write clean, fault‑tolerant, modular Python code (not just Jupyter notebooks). Good understanding of data processing pipeline optimization.
Hardware Awareness: Deep understanding of CUDA and GPU utilization to squeeze maximum performance out of Edge hardware.

GenAI Experience: Familiarity with Large Language Models (LLMs) or Vision Transformers (ViT).
Deployment: Understanding of Docker and Kubernetes (you don't need to be an expert, but you need to know how your code is shipped).
MLOps: Experience with model versioning and lifecycle management.

Meritocracy: A candid startup culture where the best ideas win.
The Playground: Access to the latest NVIDIA Hardware and cutting‑edge Generative AI tools.
Ownership: Lead a performance‑oriented team driven by autonomy and open to experiments.
Impact: Design systems for high accuracy and scalability that physically move the global supply chain.

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