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Lead AI ML Engineer

Lead AI ML Engineer

Prolegion

3 - 8 years

5 - 10 Lacs

Mumbai Navi Mumbai

Posted:1 week ago| Platform:

Apply

Skills Required

Computer science Cloud computing C++ Simulation Analytical Artificial Intelligence Machine learning Research Open source Python

Work Mode

Work from Office

Job Type

Full Time

Job Description

Position Overview

We are looking for a passionate and skilled

AI Research Engineer

to join our team and advance the field of autonomous systems. In this role, you will focus on developing cutting-edge algorithms in

Reinforcement Learning (RL)

Imitation Learning

, and

Autonomous Decision-Making

to enable robots to learn, adapt, and make decisions in complex, dynamic environments. You will work alongside other AI researchers and engineers to push the boundaries of autonomous decision-making in real-world robotics applications.

Key Responsibilities

Conduct research and development in
Reinforcement Learning
(RL) and
Imitation Learning
to enable robots to learn complex tasks through both trial-and-error and expert demonstrations.
Design and implement novel algorithms for autonomous decision-making, optimizing for efficiency, scalability, and robustness in dynamic environments.
Develop methods for combining RL with other learning paradigms, such as supervised learning, unsupervised learning, and imitation learning, to improve the performance and generalization of autonomous systems.
Work on reward engineering and exploration strategies for RL agents to enable fast and effective learning in real-world environments.
Develop and implement simulation environments for training and evaluating RL and imitation learning algorithms, focusing on tasks such as navigation, manipulation, environment exploration and gait planning with dynamic environments.
Design and evaluate approaches for transfer learning and domain adaptation to ensure that RL agents can transfer knowledge learned in one environment to new, unseen environments.
Integrate RL and imitation learning algorithms into robotic platforms, ensuring they can function in real-time with the robots sensors and actuators.
Work closely with cross-functional teams, including robotics engineers, perception engineers, and software developers, to deploy decision-making algorithms in production environments.
Perform thorough testing and validation of RL and imitation learning algorithms in both simulated i.e. Isaac Sim and real-world robotic systems i.e NVIDIA s Jetson hardware.
Continuously monitor and improve the efficiency of learning algorithms, reducing training time and computational costs while maintaining high performance.
Stay up-to-date with the latest research and advancements in reinforcement learning, imitation learning, and autonomous decision-making, applying relevant techniques to real-world applications.
Contribute to the development of internal tools, frameworks, and libraries to support the deployment and scaling of RL algorithms in production systems.

Required Qualifications

Bachelor s or Master s degree in Computer Science, Artificial Intelligence, Robotics, or a related field.
Strong background in
Reinforcement Learning
(RL) and
Imitation Learning
, with hands-on experience applying these techniques to real-world problems (3+ years of research or industrial experience).
Proficiency in machine learning frameworks such as TensorFlow, PyTorch, or JAX, with experience in building and training RL agents using Isaac Lab.
In-depth understanding of RL algorithms, including Q-learning, Policy Gradient methods, Actor-Critic, and deep RL techniques (e.g., DQN, A3C, PPO).
Experience with
Imitation Learning
algorithms, such as Behavioral Cloning, DAGGER, or GAIL, and applying them in autonomous systems.
Strong programming skills in Python, C++, or similar languages commonly used in AI and robotics.
Experience with simulation platforms like Isaac Sim (Preferred) ,Gazebo, Unity, or PyBullet for RL agent training and evaluation.
Familiarity with robotic systems, sensors, and actuators, and how to integrate AI algorithms with these hardware components.
Ability to communicate complex AI research findings and algorithmic designs clearly to both technical and non-technical stakeholders.
Strong analytical and problem-solving skills, with the ability to debug and optimize complex machine learning algorithms.
Experience working with large-scale datasets and parallel or distributed computing frameworks is a plus.

Preferred Qualifications

Experience with multi-agent reinforcement learning or cooperative decision-making in autonomous systems.
Knowledge of safe exploration techniques and reward design in RL.
Familiarity with cloud computing and distributed training infrastructure for AI and RL algorithms (e.g., AWS, Google Cloud).
Experience in deploying RL-based decision-making systems in real-world applications such as robotics, autonomous vehicles, or drones.
Contributions to open-source AI or reinforcement learning libraries, or published research in top AI conferences or journals.

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Personal Settings

Lead AI ML Engineer

Experience & Salary

Skills Required

Work Mode

Job Type

Job Description

Position Overview

AI Research Engineer

Reinforcement Learning (RL)

Imitation Learning

Autonomous Decision-Making

Key Responsibilities

Reinforcement Learning

Imitation Learning

Required Qualifications

Reinforcement Learning

Imitation Learning

Imitation Learning

Preferred Qualifications

More Jobs at Prolegion