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EdgeVerse India Private Limited
Sr Radar Perception StackEdgeVerse India Private Limited • Kolkata, IN
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Sr Radar Perception Stack

Sr Radar Perception Stack

EdgeVerse India Private Limited • Kolkata, IN
4 days ago
Job description

Company Description

EdgeVerse India Private Limited specializes in developing next-generation perception stacks designed to operate independently of specific edge devices, with a primary focus on applications for mobility & industrial automation. Our mission is to redefine the capabilities of perception technologies, ensuring versatility and precision across various use cases. Located in Bengaluru, EdgeVerse India offers a collaborative environment for individuals passionate about advancing perception technologies.


The Role

We are looking for a Radar Perception Engineer who can own the classical and learned perception stack from pointcloud to tracked objects. You will design and implement clustering, data association, and multi-object tracking algorithms, and benchmark them rigorously on real-world data. This is a hands-on engineering role; you will write production-quality code, not just research prototypes.

What You Will Do

  • Design and implement pointcloud clustering algorithms on mmWave radar pointcloud data
  • Build multi-object tracking pipelines - state estimation, data association, track management
  • Implement and tune data association algorithms
  • Develop and maintain Kalman Filter variants (KF, EKF, UKF) for object state estimation
  • Benchmark tracking performance using standard metrics
  • Contribute to dataset collection, annotation pipelines, and simulation frameworks

What We're Looking For

Must Have

  • Common sense and ability to code and maintain large codebase without the help of AI tools
  • B.Tech with 3-4 years or M.Tech with 2-3 years of hands-on experience in object tracking and multi-sensor perception
  • Strong fundamentals in estimation theory - Kalman filtering, Bayesian inference, probabilistic data association
  • Experience implementing clustering algorithms on sparse, noisy radar pointcloud data
  • Solid understanding of multi-object tracking - track lifecycle management (initiation, tentative, confirmed, deleted states), association cost matrices, gating strategies
  • Hands-on experience with data association algorithms - GNN, JPDA, MHT
  • Kalman Filter family - Linear KF, EKF, UKF, CKF etc.
  • Ability to read, explain and implement algorithms from academic papers

Advanced Tracking - Good to Have

  • Neural IMM — learning model transition probabilities from data rather than hand-tuning them
  • Extended object tracking methodology
  • Various motion models
  • Particle Filters & Sequential Monte Carlo Methods
  • ML & Deep Learning Approaches

Who You Are

  • You are comfortable with mathematical rigour - understand, implement and explain to others
  • You debug algorithms by understanding the physics and the math, not just blindly tuning hyperparameters
  • You can go from a paper to a working Python implementation quickly
  • You take ownership: if the tracking is broken, it's your problem until it's fixed - go deep as much as you can until the problem is root-caused and solved
  • You know when classical methods are sufficient and when learned approaches are justified - you don't reach for deep learning by default
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Sr Radar Perception Stack • Kolkata, IN

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