Robotics & ML Engineer – End-to-End Learning Systems

Company Description Sentientx is a robotics intelligence company focused on accelerating real-world autonomy through high-quality robot training data and agentic learning solutions. The organization is dedicated to building truly intelligent machines by treating data as the core driver of robotic superintelligence. Sentientx develops advanced systems that enable robots to perceive, learn, and act reliably in complex environments. Team members collaborate at the intersection of robotics, machine learning, and data engineering to push the boundaries of automation. Joining Sentientx offers the opportunity to shape the next era of intelligent robotic systems.
Role Description The Robotics & ML Engineer – End-to-End Learning Systems role is a full-time, on-site position based in Bengaluru. The engineer will design, implement, and optimize end-to-end learning pipelines for robotic agents, from data collection and preprocessing to model training, evaluation, and deployment on real-world platforms. Daily responsibilities include developing and testing algorithms for perception, control, and decision-making, building and refining neural network architectures, and integrating learning systems with robotic hardware and simulation environments. The role involves close collaboration with data engineers and robotics teams to curate high-quality training datasets, perform rigorous experimental analysis, and ensure robust performance in diverse scenarios. The engineer will also contribute to system documentation, code reviews, and continuous improvement of development practices.
Qualifications
Strong foundation in Computer Science and Algorithms, with experience designing efficient and scalable solutions for machine learning and robotics.
Proficiency in Pattern Recognition and Statistics to build, analyze, and improve learning models for perception and decision-making.
Hands-on experience with Neural Networks, including deep learning frameworks (e.g., PyTorch, TensorFlow) and end-to-end training workflows.
Solid programming skills in languages such as Python and C++; familiarity with robotics middleware (e.g., ROS) and simulation tools (e.g., Gazebo, Isaac Sim) is beneficial.
Background in robotics, control systems, or applied machine learning, preferably with experience deploying models on physical robots or edge devices.
Bachelor’s, Master’s, or Ph.D. in Computer Science, Robotics, Electrical Engineering, or a related field, or equivalent practical experience.
Ability to work collaboratively in a multidisciplinary team, communicate technical concepts clearly, and design reproducible experiments.
Experience with data pipelines, MLOps practices, and reinforcement learning or imitation learning is a strong plus.

Work arrangement
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