Scientist I / Ii, Robotics

Lila Sciences Lila Sciences · AI Frontier · One Charles Park, Cambridge, MA · Robotics

The role focuses on researching and developing autonomous robotic systems that integrate robotics, machine learning, and systems engineering to create intelligent physical infrastructure for scientific discovery. This involves designing and building production-grade mobile manipulation platforms, advancing robotic perception, and using simulation for optimization, ultimately translating research into real-world autonomous systems.

What you'd actually do

  1. Design and develop autonomous robotic systems for transport and workcell operations, integrating advanced path planning, navigation, and motion planning algorithms
  2. Build production-grade mobile manipulation platforms leveraging ROS/ROS2 and modular robotic architectures
  3. Advance robotic perception by integrating sensing modalities such as 3D vision, LIDAR, and tactile sensors to enable robust, adaptable task execution
  4. Use simulation environments to model, test, and optimize task planning, scheduling, and robot behaviors in diverse lab scenarios
  5. Collaborate with AI, mechanical, and software engineering teams to translate theoretical robotics research into real-world autonomous systems

Skills

Required

  • Ph.D. in Robotics, Computer Science, Mechanical/Electrical Engineering, or a related field, or equivalent research experience
  • Expertise in motion planning, path planning, and navigation for manipulation and mobile robotics
  • Proficiency with ROS/ROS2, C++, and Python, with hands-on experience building robotic systems in real-world environments
  • Deep understanding of perception systems and sensor integration (e.g., camera, LIDAR, tactile sensors)
  • Proven ability to take robotic systems from concept through to deployment

Nice to have

  • Experience with dual-arm mobile manipulation systems or other high-DOF robotic platforms
  • Familiarity with simulation tools such as Gazebo, Isaac Sim, or PyBullet
  • Background in machine learning for perception, control, or adaptive planning
  • Experience with real-time decision-making in human-robot collaboration scenarios
  • Strong publication or patent record in robotics, autonomy, or perception

What the JD emphasized

  • production-grade
  • real-world environments
  • concept through to deployment

Other signals

  • autonomous robotic systems
  • intelligent physical infrastructure
  • scientific superintelligence platform
  • novel algorithms
  • intelligent robotic solutions
  • fully autonomous workflows
  • scientific discovery
  • machine learning
  • systems engineering