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About the Role
Baselayer uses LLM-driven agents to gather and verify information about businesses from the web, treating this process as a production data pipeline. This role involves owning a segment of this enrichment surface end-to-end, focusing on extracting structured evidence from web data.
Responsibilities
- 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.
Requirements
- Shipped LLM-driven agents to production, including managing users, costs, failure modes, and on-call responsibilities.
- Proficient in async Python, including structured-data libraries, modern web frameworks, and relational databases.
- Experience with multiple frontier LLM providers and at least one agent framework, with deep knowledge of failure modes.
- Experience building or maintaining eval methodology: curated golden datasets, scoring functions, labelling guidelines, regression diagnostics.
- Experience with browser automation: headless browsers, anti-bot evasion, authenticated flows.
- Informed opinions on structured-output reliability, including JSON-schema mode, function calling, and extractor-on-top-of-text.
Skills
- 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, thinking-budget tuning, prompt-cache hit-rate work.
Location
- SF; hybrid - 4 days per week in office
Work Type
- Hybrid
Experience Level
- Senior
Salary/Compensations
- $230,000 – $340,000 + Equity
Benefits
- Flexible PTO
- Competitive compensation with equity
- 100% coverage of health, dental, and vision premiums
- 401(k) with company match
- HSA contributions
- $250 monthly gym stipend
- Transparency and radical candor
About the Company
- Baselayer is rebuilding the identity layer for institutions across the United States, creating the most complete business graph in America. They fuse public records, IRS data, sanctions lists, web signals, and fraud telemetry into a single graph that resolves any business and the humans behind it in milliseconds.
- They are trusted by over 20% of financial institutions in America and are expanding their identity layer beyond finance into every platform that needs to trust a business.
- The team is solving real-time entity resolution at scale, tackling graph AI, retrieval, and fraud-modeling problems.
- The company is at an inflection point, with the graph built and significant match rates achieved, focusing on advanced challenges like graph embeddings and real-time traversal.