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Motion planning engineer at Swaayatt Robots will primarily be responsible for implementing existing state-of-the-art or proprietary algorithms for various applications, including for autonomous driving. Secondarily, motion planning engineer will also be responsible for developing novel motion planning algorithms and navigational algorithmic frameworks, including developing decision making algorithmic pipelines, for autonomous vehicles. Typically such an engineer is expected to have the knowledge of the motion planning literature from 1995 till date, having worked on majority of, or have practical experience working deeply in at least two/three of, the motion planning paradigms listed below:

- Potential field methods
- Graph based methods (A, D, including anytime variants)
- Sampling based methods and Probabilistic Road Maps
- Local high-dimensional lattice based methods
- Local path-set methods
- HJB formulation based methods
- MPC based methods
- Various other controls and heuristic based planning algorithms

Along with that, knowledge of path tracking algorithms or controllers, and mathematical knowledge of the dynamics and kinematics of standard robotic/vehicle models is also required. Responsibilities:

- Implement existing motion planning algorithms, including reproducing results of research papers
- Write motion planning software, in C/C++, for deployment in autonomous vehicles




- Develop simulation environments to test motion planning and decision making algorithmic frameworks capabilities
- Develop or assist in the development of novel intelligent vehicle controllers using RL
- Develop or assist in the development of novel motion planning algorithmic frameworks (typically using RL)

Requirements:

- Strong mathematical foundation (linear algebra, calculus, statistics)
- Proficiency in C/C++ and Python
- Deep knowledge of data structures and algorithms
- Ability to understand and implement existing motion planning algorithms that typically use a wide range of complex mathematical concepts

Bonus qualification:

- Having robust foundation in Machine Learning
- Knowledge of convex optimization, including non-differential convex optimization
- Deep knowledge in the field of reinforcement learning
- CUDA proficiency
- Theoretical knowledge of vehicle dynamics, and numerical integration for performing FID, and tire models
- Track record of publishing papers in Tier-1 conferences in robotics or machine learning

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📌 Motion Planning Engineer (Bhopal)
🏢 Swaayatt Robots
📍 Bhopal