5-8+ years experience in product, engineering, AI workflows, founder office, or Chief of Staff roles.
Daily user of AI tools like Claude Code, Codex, OpenClaw, or equivalents.
Technical fluency in APIs, scripting, LLM tools, and automation frameworks.
Proven ability to work cross-functionally with both technical and non-technical teams.
Builder mindset, capable of turning vague problems into working prototypes.
Strong product judgment on the best tools for various needs.
Ability to navigate sensitive contexts and prioritize in a fast-paced environment.
Responsibilities
Build RoboForce's internal AI-native operating system from prototype to production.
Identify AI opportunities to create 3× leverage across the company and scale successful prototypes.
Drive the adoption of AI agents across various functions by embedding them into daily workflows.
Lead the AI Champion network to operationalize best practices across teams.
Define company-wide standards for AI tool usage and management.
Collaborate with engineering and IT on AI infrastructure and secure deployments.
Translate ambiguous AI priorities into clear systems, workflows, and execution rhythms.
Work in-office 5 days a week to enhance collaboration and rapid iteration.
Benefits
Competitive stock options/equity programs.
Health, dental, and vision insurance, plus a 401(k) plan.
Visa sponsorship and green card support for eligible candidates.
Provision of meals, a fully stocked kitchen, and regular team-building activities.
Full Job Description
We are hiring a Lead AI Native Engineer to build RoboForce's vertical agents and shared agent foundation. Reporting to the co-founder, you will turn frontier models into systems for strategy and engineering. This is a hands-on technical leadership role: you will write code, set architecture, and enable agent development-not lead People programs or organizational transformation.
Responsibilities
Build vertical agents. Own end-to-end agents for high-value workflows across research, software, hardware, data, and operations-from problem definition through production use.
Establish the agent foundation. Build reusable primitives for models, tools, orchestration, context, memory, retrieval, permissions, human approval, and long-running execution.
Create the context layer. Connect agents to trusted data through APIs, pipelines, MCP servers, and integrations with clear provenance and access control.
Own evaluation and reliability. Build benchmarks, regression tests, tracing, monitoring, and failure-analysis loops across quality, latency, cost, security, and resilience.
Advance frontier agent usage. Evaluate new models, coding agents, SDKs, and patterns, then turn useful capabilities into maintainable systems rather than demos.
Support strategic initiatives. Help company leadership apply agents and analytical systems to market and customer intelligence, partnerships, fundraising, diligence, scenario analysis, and executive decisions.
Provide technical leadership. Set architecture and engineering standards, review designs and code, and create reusable patterns for the technical team.
Requirements
5+ years in software engineering, applied AI, ML systems, or a related field, with strong zero-to-one technical judgment.
Experience at a frontier AI lab, leading AI company, or comparable team working at the edge of current model capabilities.
A track record shipping production agentic systems that real users depend on-not only prompts, prototypes, or demos.
Deep experience with frontier model APIs, tool use, orchestration, context engineering, retrieval, memory, and multi-step workflows.
Experience building evaluations, regression tests, observability, and production failure-analysis loops for AI systems.
Exceptional fluency with AI-native development workflows using Claude Code, Codex, Cursor, agent SDKs, or equivalent systems, with a rigorous understanding of where agents work and fail.
Strong product judgment: able to turn an ambiguous decision or workflow into a useful, secure, measurable system.
Requires 5 days/week in-office collaboration with the team.
Bonus Qualifications
Experience with post-training, model evaluation, inference, or research infrastructure at a frontier lab or model company.
Experience with MCP infrastructure, developer platforms, knowledge graphs, RAG, or secure enterprise integrations.
Background in robotics, autonomous systems, industrial automation, or another technically complex physical-world domain.
Benefits
Competitive stock options/equity programs.
Health, dental, and vision insurance, 401(k) plan.
Visa sponsorship and green card support for qualified candidates.
Lunches and dinners, a fully stocked kitchen, and regular team-building events.
