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Beacon AI is building an AI platform to make flying safer, more efficient, and more capable. The company is backed by top investors, has secured a dozen Department of Defense contracts, and partners with major airlines to deliver mission-critical systems. The team operates without silos, with small focused teams owning what they build and shipping quickly.
In this Staff-level role, you will work at the intersection of agentic AI, information retrieval, and production software engineering. You'll design and build AI-powered systems that help aviation professionals navigate complex, high-stakes operational decisions.
Key responsibilities include:
**Design and Build Agentic Systems**: Design and implement agent architectures involving tool use, planning, orchestration, state management, and memory. Develop effective context engineering strategies balancing relevant information, model capabilities, latency, and cost. Make thoughtful architectural decisions about when to use autonomous agents, explicit workflows, conventional algorithms, or combinations thereof. Design tool interfaces and structured model interactions that support reliable, inspectable behavior.
**Turn Complex Information into Usable Knowledge**: Develop understanding of complex document collections and data sources—their structure, semantics, authority, relationships, and scope. Translate domain knowledge into effective retrieval systems, structured representations, and agent-accessible tools. Apply information retrieval, information extraction, and knowledge modeling techniques to heterogeneous structured and unstructured data. Design strategies for handling temporal relevance, provenance, conflicting evidence, and incomplete information.
**Evaluate and Improve AI Behavior**: Develop rigorous evaluations for AI-powered systems, including representative datasets, human annotations, automated assessments, and targeted failure analysis. Investigate system-level failures across retrieval, context construction, reasoning, tool execution, and final outputs. Experiment with alternative architectures, models, and prompting strategies, using empirical results to guide decisions. Help establish engineering practices for measuring and improving the reliability of agentic systems.
**Build Systems That Work in Production**: Develop reliable services and asynchronous workflows with attention to concurrency, fault tolerance, observability, and maintainability. Integrate model inference, retrieval systems, and external data services into production applications. Build and operate containerized workloads and cloud infrastructure. Collaborate with engineers and domain experts to translate complex operational requirements into working systems.
The technology environment primarily uses Python, LangGraph, BAML, AWS Bedrock, PostgreSQL/pgvector, and event-driven AWS infrastructure. Experience with these technologies is helpful but not required.
Example problems you might work on include: designing an agentic workflow that identifies operational risks from aviation weather, airspace restrictions, aircraft-specific information, and regulatory guidance; developing retrieval and tool interfaces that allow agents to navigate multiple information sources while preserving source authority, provenance, and temporal relevance; building an evaluation framework that measures the factual completeness, correctness, and operational relevance of AI-generated analyses; investigating when an agent should perform additional research, use a specialized tool, or terminate its investigation; and improving the reliability, latency, and cost of a multi-stage AI workflow through architectural changes informed by evaluation results.
This is a hybrid role based in San Carlos, CA, requiring 3+ days per week onsite with the option to work remotely on remaining days.
**Requirements:**
- Strong software engineering fundamentals: you write maintainable production code, reason about system architecture, and understand testing, debugging, and operational reliability.
- Substantive applied AI experience: you've worked on AI systems beyond basic model API integrations and can explain their architecture, tradeoffs, limitations, and observed behavior.
- Information modeling instincts: you can understand a new domain, identify its important entities and relationships, and design software abstractions that reflect how its information is actually used.
- Experimental discipline: you test assumptions, measure results, investigate failure modes, and use evidence to guide technical decisions.
- Independent technical judgment: you can evaluate alternative approaches, recognize unnecessary complexity, and make appropriate tradeoffs.
- Clear communication: you can discuss technical decisions with both technical and non-technical stakeholders and domain experts.
Particularly valuable experience includes: aviation, aerospace, or another technically complex, safety-critical domain; computational linguistics, NLP, information science, or knowledge representation; designing systems that reason over technical documentation, regulations, or heterogeneous operational data; developing or operating distributed, event-driven, and containerized services; building AI systems in which traceability, evidence, and human oversight are important.
Note: Due to U.S. export control regulations, the company can only hire U.S. Persons (U.S. citizens, Green Card holders, lawful permanent residents, or individuals granted asylum or refugee status). No visa sponsorship or support for visa transfers is available. All work must be performed in the United States.
