Software Engineer, AI Productivity
San Francisco • FullTime
Posted 2d ago
Remote Work Policy
On-site
Employment Type
FullTime
Categories
Applied AI Engineer
About the job
Physical Intelligence is seeking an AI Productivity Software Engineer to build and deploy tools that enhance AI utilization across the company. This role involves collaborating with various teams, including engineering, research, and operations, to identify opportunities for AI-driven leverage and translate them into robust internal tools and workflows. The position is within the Runtime team, focusing on AI tooling to make AI agents, assistants, integrations, and automation effective for company-wide use. The goal is to deeply understand team workflows, build tailored tools, and drive their adoption to become integral to the company's operational rhythm.
Responsibilities
- Own AI tooling adoption across the company, identifying needs, building/integrating solutions, and driving usage.
- Develop internal AI tooling, including backend services, scripts, workflows, user interfaces, LLM integrations, and agent infrastructure.
- Enhance the usability of AI agents by managing cloud agent workflows, agent management, and internal automation for ease of use, monitoring, and trust.
- Build tools to accelerate engineering, research, and operational velocity, assisting with code development, testing, debugging, and validation, and empowering researchers with signal extraction and iteration.
- Establish best practices and enablement through playbooks, examples, onboarding, office hours, demos, and shared workflows.
- Collaborate on security and data access for AI tools, ensuring appropriate access while adhering to data boundaries, permissions, and company policies.
- Evaluate build vs. buy decisions for AI tooling, assessing commercial options and recommending adoption strategies.
- Define and measure success metrics for AI tool adoption, productivity, and satisfaction, using data to inform improvements.
Requirements
- Strong software engineering fundamentals and the ability to ship code quickly.
- Deep enthusiasm for AI tools and clear opinions on their application.
- Hands-on proficiency with AI coding workflows and modern LLM-based tools.
- Technical versatility to build backend services, internal tools, integrations, automation, and user interfaces.
- Strong product judgment with a focus on developer experience and internal tooling.
- High empathy and enthusiasm for cross-functional collaboration.
- Ability to quickly learn unfamiliar systems and operate across diverse technical domains.
- Good judgment regarding security, permissions, data access, and safe tool rollout.
- Clear communication, documentation, and teaching skills.
- Comfort in driving adoption beyond just coding.