Machine Learning Engineer

At

Virya Autonomous Technologies , we’re building intelligent, safe mobile robots designed to operate in complex, real-world environments. We’re looking for a

Computer Vision & Machine Learning Engineer

to develop robust, efficient perception systems that help our robots see, understand, and adapt to the world around them.

This role blends classical computer vision, modern machine learning, and embedded deployment. You’ll work with autonomy and systems teams to design perception pipelines that balance accuracy, efficiency, and real-time performance—while being ready to go deep when needed.

Responsibilities

Develop and optimize computer vision and deep learning algorithms for tasks such as object detection, segmentation, and scene understanding.

Design robust perception modules that work reliably in real-world factory and campus environments.

Build and manage scalable data pipelines for collection, annotation, training, and validation.

Deploy and optimize models for edge inference on embedded compute platforms.

Collaborate with autonomy and controls teams to integrate perception into the full robotic software stack.

Validate performance in simulation and on real robots; debug and iterate to ensure robustness.

Maintain clean, modular code with solid documentation and participation in reviews.

Qualifications

Bachelor’s or Master’s degree in Computer Science, Robotics, Electrical Engineering, or a related field.

3+ years of strong foundation experience in computer vision, deep learning and machine learning fundamentals.

Hands-on experience with deep learning frameworks such as PyTorch or TensorFlow.

Proficiency in Python and C++ for algorithm development and deployment.

Experience with OpenCV, NumPy, and other standard vision libraries.

Familiarity with ROS / ROS2 and perception module integration within robotic systems.

Experience with model optimization and deployment tools such as TensorRT, ONNX, or quantization workflows.

Solid debugging and system-level problem-solving skills, especially in real-time and embedded contexts.