21 days ago
Responsibilities
- Build and own production vertical agents from problem definition through deployment for workflows across research, software, hardware, data, and operations.
- Develop the shared agent foundation covering models, tools, orchestration, context, memory, retrieval, permissions, human approval, and long-running execution.
- Connect agents to trusted data through APIs, pipelines, MCP servers, and integrations with provenance and access control.
- Build evaluations, regression tests, tracing, monitoring, and failure-analysis loops covering quality, latency, cost, security, and resilience.
- Evaluate frontier models, coding agents, SDKs, and agent patterns and turn effective capabilities into maintainable systems.
- Apply agents and analytical systems to market and customer intelligence, partnerships, fundraising, diligence, scenario analysis, and executive decision-making.
- Set architecture and engineering standards, review designs and code, and create reusable patterns for the technical team.
Requirements
- At least 5 years of experience 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 operating at the edge of current model capabilities.
- Track record of shipping production agentic systems that real users depend on, beyond 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.
- Strong fluency with AI-native development workflows using Claude Code, Codex, Cursor, agent SDKs, or equivalent systems.
- Rigorous understanding of where AI agents work and fail, plus strong product judgment for turning ambiguous workflows into useful, secure, measurable systems.
- Bonus experience may include post-training, model evaluation, inference, research infrastructure, MCP infrastructure, developer platforms, knowledge graphs, RAG, secure enterprise integrations, robotics, autonomous systems, or industrial automation.
Benefits
- Competitive stock options and equity programs.
- Health, dental, and vision insurance plus a 401(k) plan.
- Visa sponsorship and green card support for qualified candidates.
- Lunches and dinners, a fully stocked kitchen, and regular team-building events.
- Requires five days per week of in-office collaboration with the team.
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