As a senior technical individual contributor, you will partner closely with leaders across the broader technical organization to design, execute, and deliver in a complex and collaborative roadmap of Trust engineering efforts.

Your expertise will be crucial in defining and executing the long-term vision and strategy for the Trust Platform, driving the architectural direction for foundational systems that enable scalable, high-quality fraud, safety, and trust decisioning across Airbnb.

You will dive deep into Trust Platform components, build system and performance tooling, and identify cross-cutting opportunities to elevate technical quality, operational excellence, and developer delight.

Champion an AI-first engineering approach, using LLM-powered agents to generate and iterate on code while focusing on problem-solving, system design, and quality oversight.

Partner with technical leaders, product managers, operations, data scientists, and Trust defense & ML teams to define and drive platform capabilities, tackle evolving fraud threats, and deliver measurable business impact.

Lead and mentor engineers across teams, raising the technical bar and developing the next generation of technical leaders within the trust engineering ecosystem.

Your expertise includes 12+ years of backend & platform engineering designing, building, and operating mission-critical distributed systems at scale.

You have a proven experience guiding multi-year technical roadmaps, aligning long-term platform vision with day-to-day execution across multiple sub-teams, prototyping to iterate on ideas, and influencing without relying on formal authority.

Exceptional communication skills, equally effective white-boarding with senior ICs, partnering with cross-functional leads (Product, Design, Data Science), and presenting strategy to executives and the broader engineering community.

Obsessed with reliability: deep experience building and improving observability, SLOs, incident response, and resilience patterns (circuit breakers, graceful degradation, chaos testing) to keep mission-critical systems running at the highest bar.

Hands-on with modern languages (e.g., Kotlin/Java/Scala) and comfortable evaluating and introducing new technologies including AI-assisted development workflows (LLMs, code generation).

Experience building developer experience & tooling is a plus, with a mindset of treating internal developers as customers and championing self-service, documentation, and delightful DX.

Experience in the Trust and Risk domain is a plus.

This position is US - Remote Eligible. The role may include occasional work at an Airbnb office or attendance at offsites, as agreed to with your manager.

While the position is Remote Eligible, you must live in a state where Airbnb, Inc. has a registered entity. Click here for the up-to-date list of excluded states.

If your position is employed by another Airbnb entity, your recruiter will inform you what states you are eligible to work from.

Our Commitment To Inclusion & Belonging: Airbnb is committed to working with the broadest talent pool possible. We believe diverse ideas foster innovation and engagement, and allow us to attract creatively-led people, and to develop the best products, services and solutions.

All qualified individuals are encouraged to apply. We strive to also provide a disability inclusive application and interview process.

If you are a candidate with a disability and require reasonable accommodation in order to submit an application, please contact us at: reasonableaccommodations@airbnb.com.

Please include your full name, the role you’re applying for and the accommodation necessary to assist you with the recruiting process.

We ask that you only reach out to us if you are a candidate whose disability prevents you from being able to complete our online application.

How We'll Take Care of You: Our job titles may span more than one career level. The actual base pay is dependent upon many factors, such as: training, transferable skills, work experience, business needs and market demands.

The base pay range is subject to change and may be modified in the future. This role may also be eligible for bonus, equity, benefits, and Employee Travel Credits.

Pay Range $248,000-$310,000 USD

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LLM & AI Pipeline Engineering - Design, build, and maintain production-grade LLM integration pipelines , including retrieval-augmented generation (RAG), prompt engineering, output parsing, and chain orchestration.

Develop and operate AI features within Jeeves's core financial products: spend categorization, document extraction, anomaly detection, financial Q&A, and automated reconciliation.

Implement structured output validation, fallback handling, and confidence scoring to ensure AI decisions meet reliability standards for financial use cases.

Evaluate and integrate AI frameworks and tools (LangChain, LlamaIndex, OpenAI API, Anthropic API, HuggingFace, vector databases) and advocate for the right tool for the job.

Establish prompt versioning and evaluation practices to ensure AI outputs remain accurate and consistent as models and data evolve.

Retrieval & Vector Search - Design and maintain vector search pipelines using databases such as Pinecone, Weaviate, or pgvector to power semantic search and RAG-based features.

Build document ingestion and chunking pipelines for Jeeves's financial data , processing invoices, receipts, policy documents, and transaction records.

Optimize retrieval quality through embedding model selection, chunk strategy, metadata filtering, and re-ranking techniques.

ML Model Serving & Operations - Collaborate with data scientists to take trained ML models from experimental notebooks to production serving infrastructure.

Build and maintain model serving endpoints with appropriate latency SLOs, input validation, and output monitoring.

Implement model performance monitoring and data drift detection to ensure production models remain accurate over time.

Support model retraining workflows by designing clean data pipelines and feature engineering that can be continuously updated.

Backend Integration & Reliability - Integrate AI services cleanly with Jeeves's backend microservices , designing clear API contracts, circuit breakers, and graceful degradation patterns.

Write high-quality, testable backend code in Python or Go/Node.js to power AI-integrated features.

Instrument AI components with structured logging, distributed tracing, latency dashboards, and alerting to ensure operational visibility.

Collaboration & Growth - Partner with Product, Backend Engineering, and Data Science to define the AI roadmap and translate requirements into reliable systems.

Contribute to a culture of quality by writing design docs, reviewing peers' AI system designs, and sharing learnings openly.

Help grow the AI engineering practice at Jeeves by establishing patterns, tooling, and best practices that the broader team can build on.

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LLM & AI Pipeline Engineering - Design, build, and maintain production-grade LLM integration pipelines , including retrieval-augmented generation (RAG), prompt engineering, output parsing, and chain orchestration.

Develop and operate AI features within Jeeves's core financial products: spend categorization, document extraction, anomaly detection, financial Q&A, and automated reconciliation.

Implement structured output validation, fallback handling, and confidence scoring to ensure AI decisions meet reliability standards for financial use cases.

Evaluate and integrate AI frameworks and tools (LangChain, LlamaIndex, OpenAI API, Anthropic API, HuggingFace, vector databases) and advocate for the right tool for the job.

Establish prompt versioning and evaluation practices to ensure AI outputs remain accurate and consistent as models and data evolve.

Retrieval & Vector Search - Design and maintain vector search pipelines using databases such as Pinecone, Weaviate, or pgvector to power semantic search and RAG-based features.

Build document ingestion and chunking pipelines for Jeeves's financial data , processing invoices, receipts, policy documents, and transaction records.

Optimize retrieval quality through embedding model selection, chunk strategy, metadata filtering, and re-ranking techniques.

ML Model Serving & Operations - Collaborate with data scientists to take trained ML models from experimental notebooks to production serving infrastructure.

Build and maintain model serving endpoints with appropriate latency SLOs, input validation, and output monitoring.

Implement model performance monitoring and data drift detection to ensure production models remain accurate over time.

Support model retraining workflows by designing clean data pipelines and feature engineering that can be continuously updated.

Backend Integration & Reliability - Integrate AI services cleanly with Jeeves's backend microservices , designing clear API contracts, circuit breakers, and graceful degradation patterns.

Write high-quality, testable backend code in Python or Go/Node.js to power AI-integrated features.

Instrument AI components with structured logging, distributed tracing, latency dashboards, and alerting to ensure operational visibility.

Collaboration & Growth - Partner with Product, Backend Engineering, and Data Science to define the AI roadmap and translate requirements into reliable systems.

Contribute to a culture of quality by writing design docs, reviewing peers' AI system designs, and sharing learnings openly.

Help grow the AI engineering practice at Jeeves by establishing patterns, tooling, and best practices that the broader team can build on.

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LLM & AI Pipeline Engineering

• Design, build, and maintain production-grade LLM integration pipelines , including retrieval-augmented generation (RAG), prompt engineering, output parsing, and chain orchestration.
• Develop and operate AI features within Jeeves's core financial products: spend categorization, document extraction, anomaly detection, financial Q&A, and automated reconciliation.
• Implement structured output validation, fallback handling, and confidence scoring to ensure AI decisions meet reliability standards for financial use cases.

Retrieval & Vector Search

• Design and maintain vector search pipelines using databases such as Pinecone, Weaviate, or pgvector to power semantic search and RAG-based features.
• Build document ingestion and chunking pipelines for Jeeves's financial data , processing invoices, receipts, policy documents, and transaction records.

ML Model Serving & Operations

• Collaborate with data scientists to take trained ML models from experimental notebooks to production serving infrastructure.
• Build and maintain model serving endpoints with appropriate latency SLOs, input validation, and output monitoring.

Backend Integration & Reliability

• Integrate AI services cleanly with Jeeves's backend microservices , designing clear API contracts, circuit breakers, and graceful degradation patterns.
• Write high-quality, testable backend code in Python or Go/Node.js to power AI-integrated features.

Collaboration & Growth

• Partner with Product, Backend Engineering, and Data Science to define the AI roadmap and translate requirements into reliable systems.
• Contribute to a culture of quality by writing design docs, reviewing peers' AI system designs, and sharing learnings openly.
• Help grow the AI engineering practice at Jeeves by establishing patterns, tooling, and best practices that the broader team can build on.

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