Machine Learning & Computer Vision Engineer

Aaryan Sheetal Murgunde; Pune District; Hybrid; INR 1,500,000 - 2,100,000; Full time

Job summary

Aaryan Sheetal Murgunde in Pune seeks a computer vision expert to run end-to-end experiments and ship models into working systems. You will lead image and video understanding tasks, develop detection, segmentation, and tracking solutions, and prepare data pipelines for training and evaluation.

Ideal candidates have strong Python and PyTorch skills, experience with Linux and GPU training, and can translate research papers into practical experiments for production systems.

Qualifications

• Proficient in Python and PyTorch
• Strong computer vision and deep learning architecture knowledge
• Hands-on experience with image or video datasets
• Experience in detection, segmentation, tracking, pose estimation or action recognition
• Ability to design controlled experiments and meaningful metrics
• Experience with Linux and GPU-based training
• Ability to read papers and translate them into working experiments
• Comfort with data collection, labeling, train/val, and model integration

Responsibilities

• Work on image and video understanding, building end-to-end experiments
• Develop object detection, segmentation, pose estimation and tracking systems
• Perform temporal analysis of actions and multi-step processes
• Leverage multimodal data for learning and verification
• Create data pipelines for collection, annotation, training and evaluation
• Integrate trained models into larger software systems

Job description

Were hiring someone with real computer vision experience detection, segmentation, tracking, pose who can run experiments end to end and ship models into working systems.

Full-time or long-term contract (3–6 months).

What you’ll work on

• Image and video understanding
• Object detection, segmentation, keypoint detection, pose estimation, and tracking
• Temporal analysis of actions and multi-step processes
• Multimodal learning using visual, sensor, and time-series data
• Failure detection, confidence estimation, and automated verification
• Data collection, annotation, training, and evaluation pipelines
• Integrating trained models into larger software systems

You’ll review research, set baselines, compare model families, dig into failures, and recommend the practical path.

What we’re looking for

• Strong Python and PyTorch
• Solid modern computer vision and deep-learning architecture chops
• Hands-on work with image or video datasets
• Experience in detection, segmentation, tracking, pose estimation, or action recognition
• Designing controlled experiments and meaningful metrics
• Linux and GPU-based training
• Reading papers and turning them into working experiments
• Comfort with data collection, labelling, train/val, and model integration

Good to have

• Detection & segmentation: YOLO, RT-DETR, Mask R-CNN
• Pose & tracking: ViTPose, Keypoint R-CNN, ByteTrack
• Video / temporal: VideoMAE, Video Swin, MS-TCN
• Learning-based control: Behaviour Cloning, ACT, Diffusion Policy
• Multimodal learning, synthetic data, domain adaptation, uncertainty, or robotics apps

Who fits

Someone who owns an experimentation track — problem, data, models, comparison, clear recommendation. We want help deciding what to build, what to test, and how to prove it works.