Computer Vision Engineer

TEEMA; San Diego (CA); On-site; USD 120,000 - 160,000; Full time

Job summary

TEEMA in San Diego seeks a skilled Computer Vision Engineer to design and implement real-time vision solutions for tracking and simulation applications. You will work with high-speed multi-camera data in a hardware-integrated environment to deliver robust performance.

You will optimize code, apply camera calibration, stereo vision, and sensor fusion, and collaborate with hardware, firmware, and ML teams to deploy and test pipelines on embedded and desktop platforms.

Qualifications

• Bachelor’s or Master’s in Computer Science, Computer Engineering, Electrical Engineering or related field.
• 3+ years of computer vision experience in real-time, product-focused environments.
• Strong Python skills with OpenCV or similar libraries.
• Solid understanding of camera geometry, calibration, and lens distortion correction.
• Experience with multi-camera systems, stereo vision, or 3D reconstruction.
• Knowledge of tracking techniques (optical flow, Kalman filters, background subtraction, deep learning).
• Experience with real-time optimization, parallel processing, or embedded CV deployment.

Responsibilities

• Develop real-time algorithms for object detection, tracking, pose estimation, and motion analysis.
• Process high-frame-rate, multi-camera data to generate accurate 3D trajectories and insights.
• Collaborate with hardware, firmware, and simulation teams to integrate vision pipelines into embedded and desktop systems.
• Optimize performance using multithreading, SIMD, and GPU acceleration.
• Apply camera calibration, stereo vision, and sensor fusion for precise spatial modeling.
• Prototype new concepts, evaluate sensors, and support field testing.
• Write clean, testable code with unit and integration testing.
• Document algorithms, workflows, and data pipelines.
• Support ML workflows including dataset versioning, experiment tracking, and deployment (Azure ML)

Skills

Python

Real-time systems

Computer vision

Education

Bachelor’s or Master’s in Computer Science/Engineering

Tools

OpenCV

CUDA

PyTorch

TensorFlow

Job description

Join a fast-moving team building real-time vision systems that power advanced tracking and simulation technology. In this role, you will design and implement computer vision solutions that track objects and motion using high-speed, multi-camera data in a hardware-integrated environment.

What You’ll Do

Develop real-time algorithms for object detection, tracking, pose estimation, and motion analysis

Process high-frame-rate, multi-camera data to generate accurate 3D trajectories and impact insights

Collaborate with hardware, firmware, and simulation teams to integrate vision pipelines into embedded and desktop systems

Optimize performance using multithreading, SIMD, and GPU acceleration

Apply camera calibration, stereo vision, and sensor fusion for precise spatial modeling

Prototype new concepts, evaluate sensors, and support field testing

Write clean, testable code with unit and integration testing

Document algorithms, workflows, and data pipelines

Support ML workflows including dataset versioning, experiment tracking, and deployment (Azure ML)

Required Qualifications

Bachelor’s or Master’s in Computer Science, Computer Engineering, Electrical Engineering, or related field

3+ years of computer vision experience in real-time, product-focused environments

Strong Python skills with OpenCV or similar libraries

Solid understanding of camera geometry, calibration, and lens distortion correction

Experience with multi-camera systems, stereo vision, or 3D reconstruction

Knowledge of tracking techniques (optical flow, Kalman filters, background subtraction, deep learning)

Experience with real-time optimization, parallel processing, or embedded CV deployment

Preferred Qualifications

C++, PyTorch, or TensorFlow experience

GPU programming (CUDA/OpenGL)

Embedded systems or real-time video pipelines

MATLAB or ROS exposure

Docker and containerized ML workflows

Azure ML DevOps pipelines for automated training and deployment