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Deccan AI

ML Engineer - Robotics

Posted 10 Days Ago
In-Office
Mountain View, CA, USA
Mid level
In-Office
Mountain View, CA, USA
Mid level
As an ML Engineer - Robotics, you'll design, train, and deploy intelligent models for autonomous systems, focusing on perception, planning, and decision-making pipelines, while collaborating with robotics engineers to evaluate model performance.
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About Us

Deccan AI is a model training and eval startup headquartered in the Bay Area. We are founded by IIT Bombay, IIM Ahmedabad and ex-Google alumni, and work with some of the top AI frontier labs in the world, e.g. Google Deepmind, Snowflake, and many more. We are backed by Prosus Ventures, and our India delivery office is in Hyderabad. Most of our roles are on-site i.e. Bay Area / Hyderabad.
About the Role

We’re looking for an ML Engineer – Robotics to design, train, and deploy intelligent models that power autonomous systems. You’ll work at the intersection of machine learning, control systems, and real-world robotics, helping to build perception, planning, and decision-making pipelines that make machines truly adaptive.

This role is ideal for someone who loves solving hard, interdisciplinary problems — combining data-driven learning with real-world constraints.

What You’ll Do
  • Develop and optimize ML models for perception, motion planning, and control.

  • Build computer vision and sensor fusion systems using camera, LiDAR, and IMU data.

  • Integrate learning-based models with robotics software stacks (ROS/ROS2).

  • Design pipelines for data collection, simulation, and reinforcement learning.

  • Collaborate with robotics and hardware engineers to deploy models in live environments.

  • Continuously evaluate model performance and robustness across diverse scenarios.

What You’ll Bring
  • 3–8 years of experience in Machine Learning, Robotics, or Computer Vision.

  • Proficiency in Python, C++, and frameworks such as PyTorch / TensorFlow.

  • Strong understanding of robotics concepts — localization, SLAM, control systems, or sensor fusion.

  • Experience with simulation environments (Gazebo, Isaac Sim, CARLA, Mujoco, or PyBullet).

  • Familiarity with reinforcement learning, imitation learning, or adaptive control techniques.

  • Solid grasp of deploying ML models in real-time or embedded environments.

  • Curiosity, grit, and passion for pushing the boundaries of embodied intelligence.

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