Lead AI Native Engineer

RoboForce is an AI robotics company developing Physical AI–powered Robo-Labor for dull, dirty, and dangerous work. The company's robots are engineered for demanding industrial environments, with a focus on real-world deployment and scalability.

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.

- 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.

- 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.

- 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.