Applied Scientist - Agents & Robotics

вчера

ШвейцарияJuniorOnsite

roboticsagentic systemssystem design

Develop and scale agentic infrastructure and systems for robot autonomy in AI and Robotics.

Our Mission Our mission is to solve the most important and fundamental challenges in AI and Robotics to enable future generations of intelligent machines that will help us all live better lives. We are looking for highly motivated Master's and PhD graduates to join our team full-time in an invigorating environment where researchers and engineers are building the future of robotics and learning-based systems. Strong candidates will bring exceptional talent with solid fundamentals in agents and system design. In this role, you will focus on developing and scaling agentic infrastructure and systems for robot autonomy. If you have a passion for robots and a desire to be part of the advancement of robot capabilities and usefulness, you will want to join us!

Build, extend, and maintain an agentic framework for managing on-robot workflows across various real-world deployment scenarios.
Create and manage internal benchmarks for the comprehensive evaluation of agentic workflows in diverse robotic tasks.
Work closely with engineers and researchers to integrate your work with powerful simulation tools and on-robot deployments.
Profile, benchmark, and eliminate performance bottlenecks in resource-constrained Linux environments to ensure production-grade reliability.

A completed Master's (MS) or PhD degree in Robotics, Computer Science, Computational Science & Engineering, or a related field.
Proven software engineering experience (e.g., industry internships, significant open-source contributions, or complex academic/industry project work).
AI/ML Foundations: Experience with PyTorch and a strong understanding of modern AI architectures (LLMs, VLMs, or Reinforcement Learning).

Sim-to-Real Experience: Experience in deploying ML workflows on physical hardware.
Vision-Language Models (VLMs): Experience integrating multimodal models for robotic perception and task planning.
Simulators: Experience testing code in simulated environments (e.g., Isaac Sim, MuJoCo).
Robotic Middleware: Familiarity with ROS2.
Robotic Perception: Familiarity with various scene representations (e.g., Gaussian Splattings, Scene Graphs, World Models, SDFs, etc.), perception frontends (e.g., DINO, SAM, SiGLIP, etc.), and approaches to scene understanding.
Robot Learning: Experience in training ML models for robot action policies, robot perception frontends, and state estimation.