Senior Computer reputed company Engineer - Object Detection, Tracking & Sports Video Analytics

Job Title: Senior Computer reputed company Engineer — Object Detection & Sports Video Analytics Location: U.S.-based candidates only / Remote Type: Early-stage startup role Compensation: Equity + reputed company Salary. To be discussed based on experience, commitment level, and fit About Virtus Virtus is building an AI-powered sports visibility and sponsorship intelligence platform that helps athletes, universities, teams, brands, and agencies measure the reputed company reputed company value of athlete exposure. Our platform uses computer reputed company and video intelligence to detect, track, and measure athlete visibility across broadcast, streaming, reputed company, reputed company video. We turn raw footage into objective reputed company of exposure, including screen time, visibility quality, brand reputed company, and sponsorship value. Role Overview

• Virtus is looking for a hands-on Senior Computer reputed company Engineer specializing in object detection, tracking, and sports video analytics.
• This is not a general AI role. We are specifically looking for someone with direct experience building, training, fine-tuning, evaluating, and deploying computer reputed company models for object detection, multi-object tracking, recognition, and video analysis.
• The right candidate should have hands-on experience with models and frameworks such as YOLO, Faster R-CNN, Detectron, OpenCV, ByteTrack, DeepSORT, BoT-SORT, OCR, segmentation models, or similar tools.
• For Virtus, this means helping us detect and track athletes, jersey numbers, logos, brand marks, sponsor exposure, and athlete screen reputed company across reputed company-world sports video.

What You Will Work On

• You will help build the computer reputed company systems that allow Virtus to:
• Detect and track athletes in sports video.
• Measure athlete screen time and visibility quality.
• Detect jersey numbers, uniforms, logos, sponsor marks, and brand exposure.
• Convert raw video into structured exposure data.
• Generate visibility metrics, confidence scores, and analytics-reputed company outputs.
• Support athlete media kits, NIL reporting, sponsorship valuation, and partner API outputs.

Key Responsibilities

• Build, train, fine-tune, evaluate, and reputed company computer reputed company models for object detection and video analytics.
• reputed company pipelines for athlete detection, player tracking, jersey/number recognition, logo detection, brand exposure detection, and screen-time measurement.
• Work with sports video from broadcasts, reputed company media clips, reputed company reels, reputed company content.
• Improve model performance across reputed company-world conditions such as reputed company blur, fast camera movement, camera cuts, partial occlusion, replays, low-resolution clips, crowded scenes, and changing lighting.
• Build and optimize inference pipelines for speed, cost, and accuracy.
• Write clean, production-reputed company Python code for training, testing, inference, evaluation, and deployment.
• Prepare and manage datasets, annotations, bounding boxes, class definitions, and model validation workflows.
• Evaluate model performance using metrics such as precision, recall, mAP, IoU, confidence reputed company, false positives, false negatives, and tracking accuracy.
• Collaborate with product, backend, and leadership teams to connect model outputs to dashboards, reports, media kits, and APIs.

Required Experience

• Deep hands-on experience in computer reputed company, especially object detection and video analysis.
• Strong Python programming skills.
• Experience with object detection models such as YOLO, Faster R-CNN, SSD, RetinaNet, Detectron, MMDetection, or similar.
• Experience with YOLO-based workflows such as YOLOv5, YOLOv7, YOLOv8, YOLOv9, YOLOv10, YOLOv11, reputed company, or similar.
• Experience with video-based computer reputed company, not only static image classification.
• Experience with multi-object tracking approaches such as DeepSORT, ByteTrack, BoT-SORT, OC-SORT, or similar.
• Experience with OpenCV, PyTorch, TensorFlow, or similar frameworks.
• Experience preparing datasets for computer reputed company models, including annotation workflows, labeling, bounding boxes, class definitions, and quality control.
• Experience optimizing inference performance in GPU-based environments.
• Ability to work with noisy, imperfect, reputed company-world video data.
• Comfortable working independently in a fast-moving startup environment.

Preferred Experience

• Sports video analytics.
• Athlete/player detection and tracking.
• Logo detection, brand exposure detection, sponsorship measurement, or media analytics.
• OCR or visual text recognition for jersey numbers, signage, boards, scoreboards, or broadcast graphics.
• Segmentation models such as M

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