Engineer III -CVML

NewSpace Research and Technologies; Bengaluru; On-site; INR 900,000 - 1,500,000; Full time

Job summary

NewSpace Research and Technologies in Bengaluru seeks a hands-on Mid-Senior Computer Vision & Machine Learning Engineer to build, adapt, validate and deploy CV/ML pipelines for vision-aided navigation on autonomous UAVs. You will own modules from data preparation to deployment, collaborating with perception, navigation, estimation, embedded and flight-control teams.

The role emphasizes practical experience with real‑time deployment on embedded platforms, ROS1/ROS2, OpenCV, PyTorch and

Qualifications

• Bachelor’s or Master’s degree in Robotics, Electrical/Electronics, CS, Aerospace or related field.
• 2+ years (Master’s) or 4+ years (Bachelor’s) in CV/ML/robotics roles with ownership.
• Strong Python and working C++ proficiency.
• Practical experience with PyTorch or similar frameworks.
• Practical experience with OpenCV and classical multi-view geometry.
• Experience training or fine-tuning CV models.
• Solid understanding of image processing and low-light enhancement.
• Experience with keypoint detection, descriptors, matching and geometric verification.
• Knowledge of camera models, calibration, PnP, homography, epipolar geometry, RANSAC.
• Understanding of coordinate frames, rotations, quaternions, timestamps and uncertainty.
• Linux, Git and debugging practices.
• Experience with ROS1/ROS2.

Responsibilities

• Develop and improve learned local-feature detection, description and matching for vision-aided navigation.
• Generate positive/negative correspondence pairs using poses, depth, and synthetic transformations.
• Improve repeatability, distribution, descriptor discriminability and matches across views.
• Apply geometric verification before using correspondences for pose estimation.
• Build datasets and pipelines including flight-test data collection, annotation and training workflows.
• Ensure robust runtime behaviour under motion blur and lighting variation.
• Export and deploy models using ONNX, TensorRT or similar.
• Integrate vision/ML outputs with robotics stacks for navigation, mapping and decision-making.
• Document designs, experiments and deployment workflows.

Skills

Python

C++

PyTorch

OpenCV

ROS1/ROS2

Jetson/edge deploy

TensorRT

Kalman-filter concepts

SLAM/VO

Education

Bachelor’s or Master’s degree in Robotics/CS/EE

Tools

OpenCV

PyTorch

TensorRT

ONNX

ROS

Docker

Job description

What This Role Offers

• Opportunity to develop learning-based vision-aided navigation systems for autonomous UAVs.
• Hands-on exposure to real-world flight testing, dataset collection, and field validation of AI-driven autonomy.
• Ownership of algorithms from dataset preparation and training through geometric validation and embedded deployment.
• Close collaboration with perception, navigation, estimation, embedded and flight-control teams.
• An aggregated scope for R&D and software development.

About the Role

The Mid-Senior Computer Vision & Machine Learning Engineer is a hands-on role focused on building, adapting, validating and deploying computer-vision and machine-learning pipelines for visual navigation.

The role requires strong practical experience in computer vision, deep learning, and real-time deployment on embedded platforms.

This is not a generic image classification or object detection role . The engineer must understand how visual algorithms produce measurements that can be consumed safely by a navigation or state-estimation system.

The engineer is not expected to independently architect the complete navigation stack but must be capable of owning individual CV/ML pipelines and delivering estimator-ready outputs.

This role is ideal for engineers who enjoy taking algorithms from research to real-life deployment.

Key Responsibilities

• Develop and improve learned local-feature detection, description and matching pipelines for vision-aided navigation.
• Generate positive and negative correspondence pairs using known poses, geometry, depth, synthetic transformations or pseudo-labels.
• Improve feature repeatability, spatial distribution, descriptor discriminability and matchability across changing viewpoints and altitudes.
• Apply geometric verification and robust outlier rejection before using learned correspondences for pose estimation.
• Build and manage datasets and data pipelines, including data collection during flight tests, annotation, augmentation, training, and evaluation workflows.
• Ensure robust runtime behaviour under real-world conditions such as motion blur, lighting variation, and sensor noise.
• Export and deploy models using ONNX, TensorRT or equivalent deployment runtimes.
• Distinguish model confidence from statistical measurement covariance.
• Integrate vision and ML outputs with robotics and autonomy stacks to enable downstream tasks such as navigation, obstacle avoidance, mapping, and decision-making.
• Document system designs, experiments, model performance, and deployment workflows to support maintainability and knowledge sharing.

Minimum Qualifications

• Bachelor’s or Master’s degree in Robotics, Electrical/Electronics Engineering, Computer Science, Aerospace Engineering, Mathematics or a related field.
• 2+ years (Master’s) or 4+ years (Bachelor’s) of industry or research experience in computer vision, machine learning, or robotics-related roles.; demonstrated technical depth and ownership are more important than a strict year count.
• Strong Python and working C++ proficiency.
• Practical experience with PyTorch or another modern deep-learning framework.
• Practical experience with OpenCV and classical multi-view geometry.
• Experience training or fine-tuning computer-vision models.
• Solid understanding of Image Processing (denoising, deblurring, contrast enhancement, and low-light image improvement)
• Experience with keypoint detection, descriptor extraction, feature matching and geometric verification.
• Understanding of pinhole, fisheye, and other relevant camera models; camera calibration; projection; homography; epipolar geometry; PnP; optical flow; and RANSAC.
• Understanding of coordinate frames, rotations, quaternions, timestamps and uncertainty.
• Understanding of camera and lens properties - shutter, aperture, etc.
• Working knowledge of Linux, Git and software debugging practices.
• Experience with ROS1 or ROS2.

Mathematical Expectations

Candidates must demonstrate an applied understanding of:

• Matrix operations, coordinate transformations and linear systems.
• Linear Algebra - Eigenvalues, eigenvectors, SVD, rotation matrices, quaternions and Jacobians.
• Probability - Conditional probability, Bayes’ rule, probability distribution.
• Statistics - Random process, covariance, correlation, and first-order uncertainty propagation.
• Confidence intervals, hypothesis testing and statistical evaluation.
• Numerical conditioning and optimization (linear and convex).

Mathematical understanding should be demonstrated through vision and navigation problems, not only theoretical definitions.

Preferred Qualifications

• Experience with VIO, visual odometry, visual localization or SLAM.
• Experience with aerial, satellite, EO or thermal/IR imagery.
• Applied knowledge of distillation using model outputs, descriptors, intermediate features, correspondences or downstream pose supervision.
• Experience in pruning or architecture simplification.
• Experience in diagnosing unsupported operators, memory-transfer bottlenecks and CPU–GPU synchronization issues.
• Experience with Jetson Orin NX, TensorRT, CUDA or NVIDIA profiling tools.
• Experience with ArduPilot, PX4, MAVLink or autonomous UAV systems.
• Experience with Docker and reproducible ML environments.
• Familiarity with estimator interfaces, innovation gating or Kalman-filter concepts.

Working Hours

• Standard working hours are 9:30 AM to 6:30 PM, Monday to Friday.
• Field-testing activities may require early-morning or extended hours, depending on mission requirements.

Compensation Range

• Competitive compensation aligned with industry standards, including performance-based incentives.
• Exact salary ranges can be customised according to experience.