Senior AI Engineer, Agentic Data Enrichment

ABOUT BASELAYER

Every day, financial institutions make consequential decisions about which businesses to onboard and trust, in order to grow with confidence. Baselayer exists to make those decisions faster, more accurate, and much less expensive. We offer the leading AI Risk Platform that automates merchant onboarding, underwriting, and fraud prevention for over 20% of the Financial Institutions in America. By combining authoritative government data, AI agents purpose-built for web analysis, and a network powered by millions of applications, Baselayer prevents billions of dollars in merchant fraud and credit risk and instantly automates good merchant applications.

ABOUT THE TEAM

We're a small team solving a big problem. Business fraud costs the financial system billions every year, and the tools built to fight it are slow, expensive, and outdated. We're changing that. You'd be joining a small, close-knit team where ownership is real, customer impact is immediate, and the work you ship matters from day one. We move deliberately, communicate directly, and care deeply about quality. If you want to look back in five years and know you worked on something that changed how trust works across major financial institutions, let's chat!

ABOUT THE ROLE

Baselayer answers questions the loan application didn't ask. For every business that crosses our queues, we need to know things that aren't on the form: what the business actually does, where it actually lives on the web, whether the people it names match the public record, and whether anything across the open web contradicts the story we were told. We answer those questions with LLM-driven agents that crawl, click, search, and extract structured evidence from across the web - and we treat this as a production data pipeline, not a research demo. We're hiring a Senior AI Engineer to own a slice of this enrichment surface end-to-end.

WHAT YOU'LL DO

• Own industry/category classification of businesses from heterogeneous signals (name, website, directory presence, reviews).
• Build and maintain discovery and verification systems for a business's real web presence - filtering aggregators, parked domains, brand collisions, and impersonators.
• Link individuals to businesses via public web evidence (e.g. confirming a named officer or employee genuinely works there).
• Develop risk/legitimacy scoring derived from web-presence signals, fed back into downstream underwriting.
• Build and evolve the shared agent infrastructure: provider-agnostic base agents, shared toolset registry (browser navigation, search, scraping, structured database lookups, scoring), eval harness, and instrumentation surface for token-and-tool tracing.
• Own model selection, agent design, prompt and tool engineering, eval methodology, and cost control across your enrichment surface.

MINIMUM REQUIREMENTS

• Shipped LLM-driven agents to production - not notebooks, not demos. Real users, real cost, real failure modes, real on-call.
• Strong async Python including structured-data libraries, modern web frameworks, and relational databases.
• Experience across multiple frontier LLM providers and at least one agent framework, with deep knowledge of failure modes.
• Built or maintained eval methodology: curated golden datasets, scoring functions, labelling guidelines, regression diagnostics.
• Browser automation experience: headless browsers, anti-bot evasion, authenticated flows.
• Holds informed opinions on structured-output reliability - when to use JSON-schema mode vs. function calling vs. extractor-on-top-of-text.

WHAT SETS YOU APART

• Web scraping at scale: anti-bot evasion, residential proxies, request fingerprinting, authenticated flows, CDN defeats.
• Eval-framework experience (e.g., LangSmith, Braintrust, Evals, or custom).
• Entity resolution / record linkage / fuzzy matching at scale.
• Browser-automation experience at the devtools-protocol level.
• Built a tool registry or toolset abstraction over multiple LLM providers.
• Cost/latency optimization: response caching, semantic caching, model routing (cheap-first then escalate), thinking-budget tuning, prompt-cache hit-rate work.

WORK LOCATION

• Based in SF; hybrid - 4 days per week in office.

COMPENSATION

• Salary Range: $195,000 – $300,000 + Equity | 0.05% – 0.25%

BENEFITS

• Time off when you need it: Flexible PTO so you can recharge without red tape.
• In-person energy: We're based in NYC & SF and meet in the office 3-5 days a week.
• Competitive compensation: We pay well and back it with equity. We want you to think and act like an owner.
• Career rocket fuel: You'll help build the foundation of a high-growth startup, working side by side with experienced founders and team members who've done it before.
• Benefits on us: We cover 100% of your health, dental, and vision premiums. No surprise deductions from your paycheck.
• 401(k) with company match: We match your contributions so your future self benefits too
• HSA contributions included: We contribute to your HSA on applicable plans, so your coverage works as hard as you do
• Stay healthy, stay sharp: A $250 annual gym stipend to help you bring your best self to work, and everywhere else
• A seat at the table: We believe in transparency, radical candor, and giving every team member a voice