Sr. Engineer III - CVML

What this Role Offers

Technical ownership of classical CV and learning-based visual navigation systems for autonomous UAVs operating in GNSS-degraded and denied environments

Opportunity to define the architecture connecting perception, state estimation, embedded computing and flight systems

Research and product-development responsibility across EO, IR, inertial, radar and RF-derived information

Ownership of dataset strategy, mathematical correctness, uncertainty modelling and system-level validation

Opportunity to lead embedded deployment on resource-constrained computing platforms

Technical leadership across computer vision, machine learning, navigation, estimation and sensor-integration teams

About the Role:

The Senior Computer Vision & Machine Learning Engineer is a senior hands-on technical role responsible for architecting and delivering learning-based visual navigation and resilient-PNT capabilities.

The engineer will own the technical direction for learned local features, day-night adaptation, geometric vision, vision-aided localization, navigation-ready perception outputs, camera-IMU integration and embedded ML optimization.

The role also includes developing multimodal navigation capabilities and statistical(-learning) methods for quality determination.

The engineer must be able to connect ML performance with geometry, uncertainty, navigation integrity, embedded constraints and field behaviour. This role requires system-level accountability in addition to algorithm development.

Key Responsibilities:

Visual-Navigation Architecture

Learned Local Features and Matching

EO/IR Data and Model Strategy

Geometric Vision and Mathematical Review

Navigation-Ready Perception and Integrity

Time Synchronization, Calibration, nadir and oblique operation

Embedded ML and System Co-Design

Multimodal ML

Technical Leadership and Validation

Minimum Qualifications:

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

6+ years (Bachelor’s) or 5+ years (Master’s) or 1+ years (PhD) or more years of relevant experience; demonstrated architecture ownership and technical depth are more important than a strict year count

Strong Python, C++, and ROS proficiency

Advanced practical experience with modern deep-learning frameworks and computer-vision libraries

Demonstrated experience building visual localization such as visual-inertial odometry, SLAM, scene matching or closely related navigation systems

Deep understanding of camera models, multi-view geometry, robust pose estimation and nonlinear optimization

Experience designing datasets and training or adapting models using domain-specific imagery

Experience defining coordinate frames, timestamps, uncertainty and health interfaces for downstream estimation systems

Experience deploying CV/ML pipelines on embedded or edge-computing platforms

Strong applied foundations in linear algebra, probability, statistics, optimization and numerical methods.

Demonstrated ownership of technical architecture, validation strategy and field-deployed systems

Ability to mentor engineers and lead cross-functional technical decisions

Mathematical Expectations

Candidates must be capable of applying and reviewing:

Matrix factorization, eigenvalue problems, SVD and numerical conditioning

Least squares, weighted least squares, convex and nonlinear optimization

Rotation matrices, quaternions, SE(3) transformations and Jacobians

Bayesian estimation, conditional probability and probabilistic graphical reasoning

Covariance modelling, cross-covariance and uncertainty propagation

Hypothesis testing, likelihood-ratio testing and confidence calibration

Sequential methods, change detection and time-series analysis

Information-based experiment design and observability

Statistical consistency and false-alarm/detection-probability analysis

Preferred Qualifications:

Experience with aerial EO, thermal/IR or satellite imagery

Experience with learned local-feature systems

Experience with radar-camera-imu calibration or multimodal sensor fusion

Experience with PyTorch, OpenCV, ONNX, TensorRT, CUDA and NVIDIA profiling tools

Experience with ROS or ROS 2, Docker and production ML pipelines

Experience with resource-constrained ARM systems

Experience with ArduPilot, PX4, MAVLink or autonomous UAV flight stacks

Experience developing or integrating Kalman filters, factor graphs or nonlinear estimators

Experience conducting UAV flight tests and defining system-level qualification criteria

Publications, patents or demonstrated research contributions in visual navigation, multimodal learning or resilient PNT

Additional Considerations for PhD Graduates

Candidates with a PhD may be considered for an enhanced designation or role variant (e.g., Lead Engineer) based on:

Depth of thesis/research experience in robotics, UAV autonomy, perception, or control systems

Demonstrated hands-on work in VIO, SLAM, sensor fusion, or advanced multimodal sensor-fusion workflows

Internships or lab experience involving UAV testing, system integration, and computer vision pipelines

Ability to take ownership of specific subsystem modules or small projects early in their tenure

Strong publication track record (IEEE Transactions, ICRA, IROS, CoRL, CVPR, ECCV, NeurIPS, ICML, ICLR)

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 will be customised according to experience

Benefits

Comprehensive health insurance

Professional development support

Detachment allowance