DevSavant seeks a Senior Computer Vision Engineer to drive the development of advanced vision systems across video-based applications. You will work on bleeding-edge technologies in pose estimation, object detection, and motion tracking—optimizing deep learning models and deploying them at scale.
What You'll Do
- Build and optimize pose estimation pipelines using HRNet, including multi-person and sports use cases.
- Implement keypoint detection models with multi-stage refinement and heatmap generation.
- Customize and train YOLOv4/X for object detection, optimizing anchor tuning, NMS, and accuracy-speed balance.
- Evaluate and compare top-down vs bottom-up pose estimation approaches.
- Apply tracking techniques including Kalman Filters, DeepSort, and custom tracking heuristics.
- Analyze temporal coherence in high-FPS sequences (30–120fps).
- Implement segmentation models for semantic and instance-level use cases using both classical and deep learning methods.
- Convert models from PyTorch → ONNX → TensorRT, leveraging ONNXRuntime for inference.
- Apply quantization, pruning, batching, and memory optimization techniques to deploy models efficiently at scale.
- Optimize GPU memory usage and runtime throughput for large-scale inference workloads.
- Manage full-frame inference for high-FPS video using tools like FFmpeg, OpenCV, and Decord.
- Implement efficient clip segmentation, sampling strategies, and multi-threaded pre/post-processing pipelines.
- Deploy models in AWS, GCP, or Azure cloud environments.
- Develop scalable, containerized inference systems (Docker, REST APIs, async queues).
- Build video ingestion pipelines with automated processing and feedback loops.
- Develop evaluation pipelines for pose estimation (e.g., PCKh, mAP), detection accuracy, and frame-wise precision.
- Integrate scoring models to map visual outputs to business or sports-specific metrics.
What We're Looking For
- 5+ years of experience in computer vision and deep learning.
- Strong track record of delivering production-ready vision pipelines.
- Proven ability to work independently and in a cross-functional remote team.
- Expert Python.
- Experience with PyTorch, OpenCV, ONNXRuntime.
- Experience with video tools FFmpeg, MoviePy.
- Experience with infrastructure Docker, REST APIs, cloud deployment on AWS/GCP/Azure.
Nice to Have
- Experience in real-time or near-real-time video inference.
- Experience in the SportsTech industry or a related field involving human motion analysis.
- Experience communicating technical concepts to clients and stakeholders.
- C++.
- TensorFlow (reference only).
- Decord.
- NVIDIA DALI.
- Experiment Tracking with W&B, MLflow, TensorBoard.
Technical Stack
- Languages: Python, C++, Bash scripting
- ML Frameworks: PyTorch, TensorFlow
- Vision & Video: OpenCV, FFmpeg, MoviePy, Decord, NVIDIA DALI
- Inference & Deployment: ONNXRuntime, Docker
- Cloud: AWS, GCP, Azure
- Experiment Tracking: W&B, MLflow, TensorBoard
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