Applied AI Engineer

Role Overview

The WoRV team builds Mobile Manipulation products that solve driving + attachment control together in brownfield environments.

This role turns customer pain points into technical challenges and converts them into Safe, Reliable, and Scalable product capabilities.

We work across the full stack — Simulation, Hardware, and the AI autonomy stack (VLA, Motion Planning, Localization, Control) — extending some capabilities as modules and others as learned policies or VLA models. The core goal is not to ship a single project, but to build product capabilities that can be reused across multiple customers.

Responsibilities

• Design systems from the perspectives of driving, attachment control, localization, data collection, teleoperation, and safety.
• Propose and implement optimal solutions using VLA, Control, RL, Motion Planning, Localization, and other techniques.
• Design and develop end-to-end systems across Navigation, Manipulation, Perception, Decision, and Action, and validate them in real field environments.
• Analyze real-world logs and failure cases to create improvement roadmaps and data collection strategies.
• Collaborate with PM / Control / Simulation / Data teams to move from PoC → Pilot → Production.
• Accumulate project outcomes as shared modules, evaluation frameworks, and data assets.

Qualifications

• Broad understanding of AI/ML with deep expertise in at least one area
• Strong Python implementation skills with real-world system development and experimentation experience
• Hands-on experience connecting at least one problem end-to-end: experiment → deployment → improvement
• Ability to reason about trade-offs beyond accuracy: latency, cost, safety, and maintainability
• Ability to structurally analyze failure root causes from logs, video, sensor data, and operational results
• Ability to learn new technologies quickly and pivot to simpler approaches when needed

Preferred Qualifications

• Awards in science, mathematics, or physics olympiads
• Experience deploying models or systems on real equipment, vehicles, or robots
• Strong research track record in machine learning or robotics (publications in top venues)
• Experience with Robotics / Autonomous Driving / Physical AI / VLA / RL / Imitation Learning
• Experience with Motion Planning / SLAM / Localization / Control Integration
• Experience with ROS2, C++, edge inference, or on-device optimization
• Experience with brownfield environments, noisy sensors, partial observability, and edge cases
• Experience with safety validation, fallback logic, data pipelines, or evaluation harnesses

Who We're Looking For

• Someone who asks "which technology fits this problem?" before anything else
• Someone who understands the difference between model performance and product performance
• Someone who can design for both immediate customer value and future scalability
• Someone who wants to build reusable product capabilities, not just local optima