Senior Computer Vision AI and ML Engineer

Your roles & responsibilities

In this role you will drive the development of cutting-edge computer vision models for perception tasks such as segmentation, object detection, and 3D understanding. Architect real-time inference pipelines, lead technical validation efforts, and contribute to prototyping and evaluating emerging technologies for robotic manipulation and situational awareness. Act as a technical mentor for junior engineers and a bridge to the AI Technical Lead.

The work model for the role is #Li_hybrid

This role is contributing to the Robotics & Discrete Automation in Bangalore India.

• Design, train, and optimize deep learning models for core perception tasksobject detection, semantic and instance segmentation, and 3D scene understandingusing multi-modal sensor data (e.g., RGB-D).
• Devise labeling strategies, and tooling for automated annotation and QA. Contribute to dataset curation, balancing, and advanced augmentation techniques to simulate real-world illumination, occlusion, and high-mix variation scenarios.
• Develop continuous monitoring frameworks to assess model performance and reliability in real-world and simulated environments. Implement uncertainty estimation and failure detection methods to support fallback and recovery strategies in dynamic, unstructured conditions.
• Conduct proof-of-concept experiments to evaluate new algorithms, tools, and methodologies in computer vision, simulation, and perception. Translate research insights into practical prototypes that can inform roadmap and architecture decisions.

• MS or PhD in Computer Vision, Robotics, Machine Learning, or a related technical field or equivalent hands-on experience building and deploying perception systems in real-world robotics applications.
• 6 to 8+years of hands-on experience building deep learning models for perception tasks such as object detection, segmentation, and 3D understanding.
• Strong proficiency with Python, C++, and deep learning frameworks like PyTorch or TensorFlow; experience with ROS2 is a plus.
• Deep understanding of multi-modal sensor data (e.g., RGB-D, LiDAR) and experience working with synthetic data, real-world datasets, and augmentation strategies for robustness.
• Experience optimizing models for real-time inference, including latency reduction, throughput scaling, and deployment in closed-loop robotic systems. Skilled in evaluation methodologies, diagnostics, and performance monitoring across both simulation and real-world environments.
• Solid understanding of robustness and reliability techniques, including uncertainty estimation, perception failure detection, and recovery strategies for dynamic, unstructured environments.
• Strong problem-solving skills, ability to work independently, and experience collaborating in cross-functional robotics teams.