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 up-to-date 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

📌 Sr. Engineer III - CVML (Bengaluru)
🏢 Newspace
📍 Bengaluru