Sr. Engineer III - CVML

Bengaluru, India · Full time · Posted Sep 16, 2026

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