• An MS/PhD in Computer Science, Operational research, Statistics, Applied mathematics, or in any other engineering discipline. PhD strongly preferred.
• Should have experience in feature engineering, hyper parameter tuning, model evaluation etc.
• Good exposure to machine learning/text mining tools and techniques such as Clustering/classification like SVM, Deep Learning networks like FRCNN, MRCNN, ResNet, FVRCNN, SalsaNext, NASnet, LSTM Reinforcement learning, and other numerical algorithms
• Should have experience in using Pandas/Numpy/ScikitLearn, Pytorch, Tensorflow, Keras, ROS, Gazebo, OpenJAUS
• Practical knowledge of automotive sensors like Camera, RADAR etc.
• Sound knowledge on the Driver assistance systems (feature functions like lane departure prevention, collision avoidance etc.)
• Strong theoretical knowledge of detection, segmentation, obstacle detection, graphical methods, probabilistic algorithms or optimization
• Hands on with visualization tools for sensors
• Knowledge in developing, calibrating and testing multi-sensor systems
• Applied knowledge of point cloud or Lidar based algorithms such as segmentation, localization, filtering
• Strong background and understanding of mathematical concepts relating to probabilistic models, conditional probability, numerical methods, linear algebra, neural network under the hood details
• Familiarity with data science toolkit such as jupyter lab/notebooks, pandas, bash scripting, Linux environment
• Publications and presentations in recognized (CVPR, NIPS, ECCV, ICML) Machine Learning journals/conferences is a Big plus
• Familiarity with any one programming (e.g., C/C++/Java) or scripting (R/Python) languages  

• Applying various Deep learning networks, statistical techniques, explore and experiment on new models through research papers or via various frameworks 
• Understanding, transforming large scale data to usable form for modelling, filtering data with generalization for later use, Cross-validating models for the requirements.
• Recommend and justify the algorithms to implement for the problems at-hand
• Implement libraries, algorithms, and tools for processing Lidar data to push the state-of-the-art in obstacle detection, object tracking, and related perception challenges
• Developing solutions for 3Cs Competitive, Cooperative and Complementing sensor framework projects
• Build perception pipeline fusing Camera, LIDAR, RADAR data for 2D and 3D object detection, scene segmentation, classification, tracking, event classification and motion predictions
• Research and develop algorithms for sensor fusion and object association across multi-sensor modalities such as one or more cameras, radars, and Lidar sensors.
• Perform multi-target tracking through the lifecycle of tracked objects including creation, splitting and merging, and termination of tracked objects.
• Enhance deep learning networks with multi-GPU and multi-node capabilities
• Interact with internal stakeholders to understand the business problems
• Applying calculus, algebra and other math to build reliable, scalable model.
• Automate algorithms in production through standardization of process and authoring best practices 
• Producing and disseminating technical and non-technical reports that detail the successes and limitations of each project.

 MS/PhD in Computer Science, Operational research, Statistics, Applied mathematics, or in any other engineering discipline. PhD strongly preferred.