Engineer III - Computer Vision & Machine Learning

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

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

• 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).

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.

• 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.

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

• Comprehensive health insurance.
• Professional development support.
• Detachment allowance.

Engineer III focused on developing, validating, and deploying computer-vision and machine-learning pipelines for UAV visual navigation. The work spans learned feature matching, geometric validation, dataset workflows, and embedded inference, with outputs designed to support navigation and state-estimation systems.

Role at a glance

Skills mentioned

What you will likely work on

• Build and refine learned local-feature detection, description, and matching pipelines for visual navigation.
• Create and curate correspondence data using pose, geometric, depth, synthetic, or pseudo-labeled information; manage collection, annotation, augmentation, training, and evaluation.
• Improve feature repeatability and matching quality across viewpoint, altitude, blur, lighting, and sensor-noise changes.
• Apply geometric verification and outlier rejection, and produce measurements suitable for pose estimation and downstream estimators.
• Export and deploy models to embedded runtimes, addressing runtime behavior and deployment constraints.
• Connect vision and ML outputs with robotics and autonomy systems supporting navigation, mapping, obstacle avoidance, and decision-making.
• Document designs, experiments, model results, and deployment workflows.
• Participate in flight testing, data collection, and field validation.

Good fit signals

• You bring practical computer-vision and deep-learning experience, including training or fine-tuning vision models.
• You can reason about geometric measurements and their safe use in navigation or state-estimation systems, beyond image classification or detection alone.
• You have hands-on Python and working C++ skills, plus practical experience with OpenCV, multi-view geometry, and an ML framework.
• You are comfortable taking an algorithm from dataset preparation through validation and embedded deployment.
• Experience with visual odometry, aerial imagery, UAV stacks, or embedded acceleration tools would be helpful, but is preferred rather than required.