Mechanical Engineer

The Role

At Config, we are building the data and foundation models for general-purpose robots that can perform useful two-handed tasks in real-world settings.

A central challenge in this effort is not just learning from data, but how that data is generated — specifically, how interactions are defined, captured, and executed in the physical world.

This is where the end-effector — the part of the system that directly interacts with objects and the environment — becomes critical. It determines how interactions with the world are executed and captured as data, serving as a common interface across different embodiments, including both robotic and human-operated interfaces.

Designing this interface is fundamentally a dual problem: the end-effector must be effective for real-world manipulation, while also producing high-quality, learnable data for training robot foundation models. These requirements can be in tension, and resolving that tension is a core part of the problem we are solving.

As a Mechanical Engineer, you will design and build robot end-effectors and/or human-operated grippers used for large-scale data generation. Your work will directly shape how robots learn, by defining how interactions are represented, captured, and executed in the physical world.

This is a unique opportunity to work at the interface between mechanical systems and AI — where hardware design directly determines the quality of data and, ultimately, the capability of robot foundation models.

Most robot learning systems are bottlenecked not by models, but by the quality of interaction data — and that quality is defined at the interface between humans, robots, and the physical world.

In this role, you will define that interface.

If you're interested in building the physical foundation for robot intelligence — and seeing your work directly translate into real-world capability at scale — we'd love to hear from you.

** This JD is designed to cover multiple levels.

Level: We are open to candidates from interns to senior, staff, and senior staff professionals. Level, scope, ownership, and compensation will be calibrated based on experience.

When applying, please specify the level that best reflects your experience and interests. We're happy to calibrate scope and leveling throughout the interview process.

Areas You May Contribute To

• Design and prototype robot end-effectors for bimanual manipulation
• Design and develop human-operated grippers that capture robot-aligned action signals
• Build hardware systems used in large-scale data collection pipelines
• Ensure designs are optimized for:
  • precision and fidelity of interaction signals
  • repeatability and robustness
  • usability for human operators
• Rapidly iterate through hands-on prototyping and testing
• Collaborate closely with AI scientist and engineers to align hardware design with model requirements
• Own the full lifecycle: concept → design → fabrication → testing → iteration

What We're Looking For

• Strong background in mechanical engineering or robotics hardware
• Experience designing and building actuated mechanical systems
• Proficiency in 3D modeling and CAD tools (e.g., SolidWorks, Fusion 360, Onshape)
• Hands-on experience with rapid prototyping (CNC, 3D printing, machining, etc.)
• Ability to move quickly from idea to working system
• Strong intuition for mechanical reliability, tolerance, and usability
• Comfortable working in a highly iterative, experimental environment
• Excellent communication and collaboration skills in Korean and written English

For senior-level candidates, we typically look for 5+ years of relevant research or engineering experience, or equivalent evidence of exceptional technical ownership. For staff and senior staff-level candidates, we look for a stronger track record of independently defining technical direction, driving ambiguous projects, mentoring others, and delivering high-impact systems or research outcomes.

Bonus Points

• Experience with robot grippers or end-effectors
• Familiarity with actuation systems, force/torque sensing, or haptics
• Experience building human-in-the-loop systems
• Exposure to robotics, control, or machine learning systems
• Prior experience in fast-moving startup or research-driven engineering environments