[US] Forward Deployed Engineer

At RLWRLD , we are solving one of the most critical bottlenecks in robotic automation: true physical dexterity. Our state-of-the-art Robotics Foundation Models (RFMs) power autonomous systems capable of complex manipulation, consistently topping benchmarks like the RoboCasa Kitchen Leaderboard . We are one of the few RFM companies actively executing real-world deployments in customer facilities across Korea, Japan, and the US.

Work with a team that has deep expertise in Robotics, and AI. Founders are serial deep tech entrepreneurs, having exited previous deep tech start-ups to companies like Intel.

What You'll Do

• Own the Stack: Drive the entire deployment lifecycle, taking responsibility for data collection, model fine-tuning, physical system deployment, troubleshooting, and production observability and fixes.
• Solve Field Issues: Rapidly isolate and patch hardware, software, and perception failures under dynamic demo and production conditions.
• Bridge R&D and Ops: Collect raw operational data and failure modes from the field to directly inform our core foundational model training and architecture.

Qualifications

• Field Experience: 2–3+ years deploying robotics or automation solutions in factory or logistics settings.
• Scrappy Mindset: Early-stage startup experience with a hands-on, proactive approach to debugging complex hardware and software integrations.
• Perception Stack: Working knowledge and practical debugging experience with perception hardware (hi-fi optical cameras, depth perception sensors, pressure/tactile sensors).
• VLA Exposure: Basic modeling understanding and practical exposure to Vision-Language-Action (VLA) architectures (academic or lab experience is fully valid).
• Travel Flexibility: Willingness to travel 50%–75% to customer sites across the US and adapt to unpredictable field deployment hours.
• Korean Fluency: Functional or professional proficiency in Korean (required for 1–2 roles on this team to interface with international teams).

Nice-to-Haves & Language

• Compute Infrastructure: Hands-on experience with GPU compute stacks and edge deployment environments.
• Base Salary: $180,000 – $220,000 - commensurate with experience.
• Equity: Early-stage equity grant.
• Benefits: Premium medical, dental, and vision coverage included.