Elayne

Estate settlement, from verified discovery to distribution

About the role

We're looking for a Senior Applied AI Engineer to build the intelligence layer behind Elayne.

Estate settlement is an unusually hard AI problem. The truth about an estate is scattered across wills, tax records, deeds, bank statements, court filings, credit data, government databases, and documents that frequently disagree with each other. We need systems that can understand all of it, connect what belongs together, know when they might be wrong, and show exactly where every answer came from.

You’ll own that problem end to end: document intelligence, entity resolution, agentic workflows, evaluation, provenance, and the infrastructure that makes probabilistic systems reliable enough for consequential financial and legal work.

The challenge isn’t getting an LLM to produce an answer. It’s building a system we can trust when the answer matters.

You’ll work directly with the CEO and Head of Engineering, with enormous freedom to determine how we solve these problems and what the AI architecture of Elayne becomes.

What you’ll build

• An AI system that understands an estate: extract and reconcile people, accounts, assets, liabilities, relationships, and legal facts across thousands of messy documents and external data sources
• Financial discovery workflows: build agents and tool integrations that investigate fragmented financial and government data, identify potential assets, surface missing information, and recommend next steps
• Traceable outputs and human review: connect answers to supporting evidence, validate critical facts, identify uncertainty and conflicting sources, and route consequential decisions for human review
• Evaluation and monitoring: build evaluation datasets, regression tests, and production monitoring to measure quality, understand failures, and assess changes to prompts, models, and pipelines
• Production AI infrastructure: own model selection and routing, data pipelines, latency, cost, retries, and failure recovery so these systems work reliably in real workflows

Errors here have real consequences: a missed account, an incorrect court filing, or a delayed inheritance. You'll build systems that help families make sense of decades of financial information, with accuracy, traceability, and clear paths for review built into the architecture.

Who we’re looking for

• You've shipped and operated AI systems. You've owned at least one production system involving document processing, extraction, retrieval, or agents, from initial development through evaluation, deployment, and ongoing improvement
• You can measure and improve reliability. You can explain how you identified failure cases, evaluated quality, caught regressions, and decided when a system needed human review
• You're a strong Python and backend engineer. You can design and build the APIs, data pipelines, services, and infrastructure around models, and make sound tradeoffs across accuracy, latency, cost, and maintainability
• You work well with ambiguous data. You're comfortable investigating conflicting documents, matching records across sources, and developing evaluation criteria when the correct answer is difficult to establish
• You take ownership of outcomes. You can turn an open-ended problem into a working system, explain architectural decisions clearly, collaborate closely with the team, and follow through on how the system performs for users

How we work

We’re small, fast, and close to the people using what we build. Product decisions can start with a family conversation, a rejected filing, an institutional edge case, or something we had to do manually that software should absorb next.

Everyone here owns outcomes, not tickets. We communicate directly, move quickly, and care deeply about impact and getting the details right.

Location & compensation

NoMad, NYC office

$250,000 to $300,000 + meaningful equity

Medical, dental, and vision insurance + 401(k)

Equal opportunity

Elayne is an equal opportunity employer. We evaluate qualified applicants based on ability, experience, and potential, and do not discriminate on the basis of any protected characteristic under applicable federal, state, or local laws.

• Intro conversation: get to know each other, the role, and what we’re building
• Technical interviews: ML systems design session and project deep dive
• Office session: a working session on a live problem and time with the team
• Offer: we check references, then move fast