Senior Computer Vision Engineer – Human Pose & Biomechanics
Senior Computer Vision Engineer – Human Pose & Biomechanics
Location: Remote (Global) Type: Full-Time or Contract Company: Texas Sports Academy
About the Role
We are building an AI-powered training app with elite volleyball leadership (including University of Texas coaching staff).
The goal:
An app that watches an athlete perform drills and provides intelligent, biomechanically sound feedback on their form.
This is applied AI at a high level — not research for research’s sake.
We need a senior engineer who understands:
• Human pose estimation
• Temporal modeling
• Video pipelines
• Applied deep learning
• Biomechanics-driven feature extraction
What You’ll Build
• Video ingestion pipeline
• Pose estimation integration
• Joint angle calculation systems
• Movement scoring models
• Feedback generation engine
• Scalable architecture for mobile + backend integration
You will be a foundational technical architect of this product.
Required Experience
• 5+ years in computer vision or applied ML
• Strong Python skills
• Experience with human pose estimation frameworks (MediaPipe, OpenPose, MoveNet, BlazePose, HRNet, etc.)
• Experience processing and analyzing video data
• Deep learning experience (PyTorch or TensorFlow)
• Experience designing production ML systems
You must understand:
• Joint angle computation
• Temporal smoothing
• Movement sequence modeling
• Feature extraction from keypoints
• Real-world model limitations
Bonus Points
• Athletic or sports background
• Experience building mobile ML systems
• Experience deploying ML to edge devices
• Experience with 3D pose estimation
• Startup experience
What Success Looks Like
Within 90 days:
• Working squat grading prototype
• Clear pose-based feature extraction framework
• Reliable joint angle calculations
• Movement scoring logic
• Architecture roadmap for volleyball drill analysis
Take-Home Evaluation
You will build a minimal squat grading app:
Requirements:
• User uploads squat video
• Extract keypoints
• Calculate:
• Knee angle
• Hip angle
• Depth
• Back angle
• Output:
• Score (1–10)
• 3 actionable improvement suggestions
Deliverables:
• GitHub repo
• README explaining:
• Model choice
• Tradeoffs
• Scaling plan
• Limitations
Time expectation: 6–8 hours.
Compensation
Competitive. Open to global talent. Contract or full-time available.
We are not looking for someone who has “experimented” with pose estimation.
We are looking for someone who can build a real product.
Job Type: Full-time
Pay: $250,000.00 - $300,000.00 per year
Work Location: Remote
Apply Now
Apply Now
Location: Remote (Global) Type: Full-Time or Contract Company: Texas Sports Academy
About the Role
We are building an AI-powered training app with elite volleyball leadership (including University of Texas coaching staff).
The goal:
An app that watches an athlete perform drills and provides intelligent, biomechanically sound feedback on their form.
This is applied AI at a high level — not research for research’s sake.
We need a senior engineer who understands:
• Human pose estimation
• Temporal modeling
• Video pipelines
• Applied deep learning
• Biomechanics-driven feature extraction
What You’ll Build
• Video ingestion pipeline
• Pose estimation integration
• Joint angle calculation systems
• Movement scoring models
• Feedback generation engine
• Scalable architecture for mobile + backend integration
You will be a foundational technical architect of this product.
Required Experience
• 5+ years in computer vision or applied ML
• Strong Python skills
• Experience with human pose estimation frameworks (MediaPipe, OpenPose, MoveNet, BlazePose, HRNet, etc.)
• Experience processing and analyzing video data
• Deep learning experience (PyTorch or TensorFlow)
• Experience designing production ML systems
You must understand:
• Joint angle computation
• Temporal smoothing
• Movement sequence modeling
• Feature extraction from keypoints
• Real-world model limitations
Bonus Points
• Athletic or sports background
• Experience building mobile ML systems
• Experience deploying ML to edge devices
• Experience with 3D pose estimation
• Startup experience
What Success Looks Like
Within 90 days:
• Working squat grading prototype
• Clear pose-based feature extraction framework
• Reliable joint angle calculations
• Movement scoring logic
• Architecture roadmap for volleyball drill analysis
Take-Home Evaluation
You will build a minimal squat grading app:
Requirements:
• User uploads squat video
• Extract keypoints
• Calculate:
• Knee angle
• Hip angle
• Depth
• Back angle
• Output:
• Score (1–10)
• 3 actionable improvement suggestions
Deliverables:
• GitHub repo
• README explaining:
• Model choice
• Tradeoffs
• Scaling plan
• Limitations
Time expectation: 6–8 hours.
Compensation
Competitive. Open to global talent. Contract or full-time available.
We are not looking for someone who has “experimented” with pose estimation.
We are looking for someone who can build a real product.
Job Type: Full-time
Pay: $250,000.00 - $300,000.00 per year
Work Location: Remote
Apply Now
Apply Now