Overview

We are looking for a senior Applied AI/ML Engineer with strong hands-on experience in Computer Vision and video systems to help lead the development of production-grade AI solutions for video understanding, detection/segmentation, tracking, multimodal reasoning and model-assisted annotation.

Responsibilities

• · ONNX, TensorRT, Triton Inference Server, CUDA/performance profiling, distributed training or multi-GPU inference. • · Experience integrating model outputs into human-in-the-loop annotation platforms such as Tensor
• Act, Encord, CVAT, Label Studio or equivalent.

Qualifications

• Strong Python and hands-on PyTorch experience, including model training, fine-tuning, debugging and experiment design.
• Solid practical experience in Computer Vision with real model-development work across at least two of the following: object detection, segmentation, tracking, pose estimation, video understanding, action recognition/localization or related temporal vision problems.
• Hands-on experience processing real image/video data using OpenCV, FFmpeg or equivalent tooling, including frame/clip preparation, augmentation and data-quality handling.
• Ability to design datasets and evaluation protocols correctly: train/validation/test construction, leakage prevention, class imbalance, hard negatives, gold-set creation and reproducible benchmarking.
• Strong model-evaluation and failure-analysis skills; able to reason about errors caused by motion blur, occlusion, domain shift, lighting, viewpoint changes, tracking drift or poor segmentation boundaries.
• Ability to compare multiple technical approaches rather than simply integrate a single popular model, and to explain architecture decisions using measurable quality, latency and reliability trade-offs.
• Ability to provide technical guidance to junior engineers while remaining hands-on with code, experiments and debugging.
• Multimodal/VLM or open-vocabulary vision experience, including vision-language grounding, video-language understanding or multimodal retrieval.
• Egocentric/first-person video, Physical/Embodied AI, hand-object interaction, manipulation data or annotation-automation experience.
• RGB-D/depth, 3D vision, camera calibration, SLAM/VIO, pose estimation, reconstruction or robotics perception.
• ONNX, TensorRT, Triton Inference Server, CUDA/performance profiling, distributed training or multi-GPU inference.