AI Engineer – Computer Vision

Role Overview

As an AI Engineer, you’ll lead the design, development, and deployment of advanced computer vision models for human motion understanding and video analytics. You’ll work across the full lifecycle of AI development — from data pipelines to inference optimization — helping ship features that power our next-generation fitness intelligence systems.

Key Responsibilities

1. Own the end-to-end lifecycle of computer vision models: from experimentation to deployment and monitoring.
2. Build and optimize deep learning models:
   i. Pose estimation and skeleton tracking
   ii. Activity recognition and classification
   iii. Posture correction and rep counting
3. Apply transfer learning, fine-tuning, and knowledge distillation techniques for performance and generalization.
4. Design scalable pipelines for video data ingestion, annotation, preprocessing, and augmentation.
5. Integrate AI modules into cloud-based (Azure/AWS) environments using REST APIs or microservices.
6. Optimize model inference using ONNX, TensorRT, and quantization strategies for real-time or edge deployment.
7. Collaborate cross-functionally with product, design, and frontend/backend teams to align on deliverables and timelines.
8. Stay up to date with the latest in vision research and evaluate new techniques for production integration.

Required Skills

1. Bachelor’s or Master’s degree in Computer Science, AI, Machine Learning, or related discipline.
2. 2+ years of experience building and deploying computer vision models in production environments.
3. Proficient in Python, with deep experience in PyTorch and/or TensorFlow.
4. Hands-on expertise in models such as:
   a. YOLOv5/YOLOv8
   b. Vision Transformers
5. Experience working with pose estimation frameworks (Mediapipe, OpenPose, Detectron2).
6. Strong understanding of camera calibration, 3D geometry, and real-time motion tracking.
7. Experience integrating models into cloud environments (Azure, AWS) using APIs or containers.
8. Familiarity with tools such as OpenCV, MMAction2, DeepSort, and ONNX/TensorRT.

Preferred Skills

• Experience in real-time video analytics, edge AI, and system optimization.
• Familiarity with CI/CD pipelines, Docker, and monitoring tools for deployed models.
• Prior experience in the fitness or healthcare domain with time-series or movement data.
• Strong grasp of system-level design: latency tradeoffs, hardware acceleration, andscalability.

What You’ll Gain

• A key role in shaping the next generation of intelligent fitness systems.
• Flexible work environment with learning and mentorship.
• Autonomy, ownership, and the opportunity to deploy models used by real users.
• A fast-paced environment with exposure to the full AI pipeline, from data to deployment.