Founding Full-Stack Engineer

Context Management API

Build the context layer AI systems need. Systems that maintain structured context across workflows, self-heal when data changes, and compound knowledge over time.

New Data Access Patterns

Design semantic query interfaces that replace SQL for AI agents. Build retrieval pipelines that reason about context before querying. Create systems that understand business semantics and go beyond data schemas.

Self-Healing Data Models

Architect feedback loops that automatically improve data models based on usage. Systems that detect when context breaks and fix it automatically. Knowledge graphs that evolve as the business evolves.

Semantic Modeling Infrastructure

Users need to be able to improve/expand their data model without data experts. Build the semantic layer that translates business questions & existing reports into precise, verifiable queries. Identity resolution across 50+ enterprise systems. Systems that learn customer language patterns and map them to business outcomes.

What We're Looking FOR

Required

• 3+ years building production systems

• Experience with retrieval systems, embeddings, vector databases, LLMs or knowledge graphs

• Production ML experience: monitoring, versioning, evaluation frameworks

• Experience with LLM orchestration (LangChain, LlamaIndex) and multi-agent systems

• Familiarity with semantic layers (dbt), data warehouses (Snowflake, BigQuery), enterprise data systems

Nice to Have

• Enterprise data systems (Snowflake, BigQuery, Salesforce, Segment, Gong)

• Multi-agent systems (LangGraph, CrewAI) or workflow orchestration (Airflow, Prefect)

• Knowledge graphs, graph databases, semantic layer tools (dbt, Cube)

• Real-time data pipelines and streaming architectures

TECH STACK

Core: Python (primary), TypeScript/JavaScript, SQL

LLMs: Anthropic Claude API, OpenAI, in-house frameworks

Knowledge Graphs/RAG

Data Infrastructure: Snowflake/BigQuery/Redshift, dbt, Kafka/Pulsar, Reverse ETL (Hightouch, Census)

Enterprise Integrations: Salesforce, HubSpot, Segment, Gong, Slack, Zendesk, Mixpanel/Amplitude

COMPANY CONTEXT

Stage: $3.3M pre-seed, proven product-market fit, growing adoption

Team: Small senior team from Uber, Lyft, OM1, Capchase. MIT, Princeton, UCSD. You'll be engineer #5-6. Direct collaboration with founders & customers

Culture: First-principles debate. Ship multiple times a day. Rapid iteration. In-person in NYC with quarterly off-sites.

Compensation: $140K-220K base + meaningful equity. Early-stage upside in proven company.

Jai, Saf, & Chirag

Co-founders of Deepline

Compensation

The base pay range for this role is $140,000 – $260,000 per year.

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Req ID: R1