Member of Technical Staff
Pubblicato il 07-09-2026 - Hire Feed in Italia
Role: Member of Technical Staff - Robotics Lab (Remote)
Location: Remote (Work from Anywhere)
Job Type: Full-Time
Payout: $400K - $1500K/yr
Role Overview:
We are hiring for one of our clients, seeking a Member of Technical Staff (Robotics Lab) to work on a full-time basis. The role involves designing, developing, and deploying robotics systems with a focus on scalable autonomy and real-time control. Candidates will collaborate with cross-functional teams to integrate hardware and software solutions for next-generation robotic platforms.
Key Responsibilities:
• Design and implement control algorithms for robotic systems, including kinematics, dynamics, and path planning.
• Develop and maintain software for robot perception, localization, and mapping using sensor fusion techniques.
• Collaborate with hardware engineers to integrate sensors, actuators, and embedded systems into functional robotic prototypes.
• Optimize robotic performance for efficiency, reliability, and safety in dynamic environments.
• Document technical specifications, test results,
and deployment procedures for internal and external stakeholders.
Required Skills & Qualifications:
• Proficiency in robotics frameworks such as ROS, ROS 2, or similar platforms.
• Strong programming skills in C++, Python, or other relevant languages for robotics applications.
• Experience with real-time control systems, embedded software, and hardware-in-the-loop testing.
• Knowledge of computer vision, SLAM (Simultaneous Localization and Mapping), or sensor fusion techniques.
• Familiarity with simulation tools like Gazebo, CoppeliaSim, or NVIDIA Isaac Sim is required.
• Experience with robotic operating systems, middleware, and communication protocols (e.g., CAN, Ethernet, UDP).
• Ability to design and analyze algorithms for motion planning, obstacle avoidance, and task execution.
• Experience with version control systems such as Git and collaborative software development workflows.
• Understanding of machine learn