Strong fit for AI systems engineers who build production agentic systems and have experience with frontier model APIs, working in industrial robotics and requiring in-office collaboration.
Derived from job-description analysis by Serendipath's career intelligence engine.
Original posting from Embedding Vc via Ashby
Why RoboForce
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.
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.
Source: Embedding Vc careers (Ashby)