ML Engineer – Automatic Computer Use; Windows

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• Design, train, and deploy ML models for computer usage and automated task execution, including UI understanding, system control, and workflow automation
• Build and scale Large Action Models (LAMs) that translate user intent into structured plans, tool calls, and executable actions
• Develop agentic AI pipelines that combine perception (screen, UI, system state), reasoning, planning, and execution with strong reliability guarantees
• Implement and evaluate action execution frameworks, including tool orchestration, sandboxed execution, rollback, and failure recovery
• Train and fine-tune multimodal models (LLMs, VLMs) for screen understanding, element grounding, and instruction-following
• Partner with OS, application, and platform teams to integrate AI agents deeply into Windows and cross-device environments
• Build evaluation systems for task success, robustness, latency, and safety in real-world computer usage scenarios
• Collaborate with product, UX, and security teams to ensure safe, transparent, and controllable AI behavior
• Stay at the forefront of agentic AI, tool-using LLMs, large action models, and autonomous execution systems

• At least 5 years of experience in ML engineering, applied AI, or intelligent systems
• Bachelor’s Degree in Computer Science, Engineering, or a related technical discipline
• Strong experience with LLMs and/or multimodal models for reasoning, planning, or control
• Hands‑on experience building end‑to‑end ML systems, not just models (training → evaluation → deployment)
• Proficiency in Python and experience with modern ML frameworks (PyTorch, JAX, Tensor Flow)
• Experience shipping ML‑powered features into production products

• Experience working on computer usage agents, UI understanding, or automated workflow execution
• Familiarity with Large Action Models (LAMs), tool‑using agents, function calling, or planner–executor architectures
• Experience with multimodal learning, including vision + language grounding
• Knowledge of reinforcement learning, imitation learning, or human‑in‑the‑loop training
• Experience deploying ML systems on Windows, Android, or edge devices
• Strong intuition for failure modes, safety, and reliability in autonomous systems
• Passion for building AI that actually does things, not just talks about them

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