Design and scale large-scale backend architecture, search, and recommendation systems for a well-funded startup. Lead technical discovery on emerging tech and build evaluation pipelines. Mentor engineers and drive cross-team architectural clarity.
Key Highlights
Key Responsibilities
Technical Skills Required
Benefits & Perks
Job Description
Comp: $200-280k base, bonus, full health, 401k, and lots more
Hybrid: 3 days/week in Manhattan
Our client is a well-funded startup that maps the global startup landscape so investors, corporate development, and GTM teams so they can find breakout companies before they're obvious. Trusted by leading AI labs, VCs, and fintechs, they're growing 100%+ YoY and are on a mission to connect billions in capital with the founders who deserve it.
About the team
Their team powers search, research, and evaluation across tens of millions of companies, hundreds of millions of operators, and tens of thousands of investors. They've built an industry-leading agentic layer for startup research, and we're defining the playbook for AI-first product experiences.
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The role
- Own end-to-end architecture of our sourcing engine: ingestion, ranking, personalization, and model orchestration
- Define frameworks for retrieval, reranking, and LLM-driven evaluation
- Lead technical discovery on emerging tech (memory systems, embeddings, agentic frameworks) and build fast, reliable evaluation pipelines
- Partner with product/design to shape intuitive, AI-native UX patterns
- Mentor engineers and drive cross-team architectural clarity
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Tech stack: LangChain, LangGraph, Groq, Terraform, React, TypeScript, dbt, PostgreSQL, Elasticsearch, GCP, Python, Go
What we're looking for
- 7+ years building distributed systems or large-scale backend architecture
- Track record designing and scaling search, recommendation, or ranking systems
- Clear progression at high-growth, VC-backed startups; demonstrated technical leadership/mentoring
- CS or related technical degree from a strong university
- Deep experience with data-intensive, asynchronous, or event-driven systems
- Hands-on leadership of ML/LLM integrations in production
- Strong product instincts and communication skills
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Not a fit: ML infra, research scientist, or ML ops-focused backgrounds
Why join: Work on a genuinely hard technical problem (one of the largest startup knowledge graphs in the world) at a company still early in its growth curve, with outsized influence over product direction.
Open to US citizens, OPT, H1B transfers, new H1B, and TN
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