About NewSpace:

We Build Mission-Ready, Cutting-Edge Intelligent Autonomous Systems

From battlefield supremacy to stratospheric dominance, NewSpace Research & Technologies (NRT) is redefining aerospace and defence with intelligent autonomous systems built for the world’s most demanding missions. Trusted by the Indian Armed Forces across all services, NRT delivers breakthrough unmanned platforms that enable speed, survivability, multi-domain mission flexibility, and strategic superiority.

NewSpace Research & Technologies (NRT), founded in 2017, is a global leader in swarming technologies, autonomous robotics, and advanced aerospace systems for defense applications. With a workforce of over 500 professionals across 12 facilities worldwide—nearly 70% focused on research and development—NRT has designed and fielded more than 15 proprietary multirotor and fixed-wing platforms. The company’s core expertise spans large-scale UAV swarms, AI-enabled autonomous coordination, and persistent unmanned systems, all validated through rigorous operational deployment in real-world conflict environments.

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
- Prospect 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)

📌 Computer Vision Engineer (Bengaluru)
🏢 NewSpace Research & Technologies
📍 Bengaluru