---
title: 'Founding Go-to-Market Engineer (Contract-to-Hire) at deepline.com'
canonical: 'https://feeny.ai/job/founding-go-to-market-engineer-contract-to-hire-deepline-com-new-york-m4z0sb9zrzs8'
type: 'job'
last_seen: '2026-09-11'
---

# Founding Go-to-Market Engineer (Contract-to-Hire) at deepline.com

- **Company:** deepline.com
- **Location:** New York, NY
- **Compensation:** $140k–$260k
- **Employment:** full-time
- **Work type:** onsite
- **Posted:** 2026-03-17
- **Last confirmed live:** 2026-09-11
- **Apply:** https://jobs.gem.com/deepline-com/am9icG9zdDo8Or3IxnO-iFvMNhKk7uXS

## Job description

Founding GTM Engineer

Location: New York City Type: Full-time

## ABOUT DEEPLINE

Deepline is the operating system for GTM execution. We turn operator intent into governed execution and measurable outcomes. Not more dashboards. Not more automations. The backend for GTM engineering that makes your GTM stack actually work. [Our vision is "ambient automation" that exists & solves problems before you know they exist.](https://aero-ai.notion.site/Deepline-Mission-Vision-Values-218da8d1d8eb80678de2c1d0f4c43c65)

We're building the universal API for B2B businesses. Replace 20+ API calls to dozens of tools with a single call to Deepline's Context API.

Deepline is a context manager that understands how the real-world works, with an intent compiler that turns context & natural language into outcomes with guardrails and observability.

- Team: Small senior team from Uber, Lyft, OM1, Capchase. MIT, Waterloo, Berkeley, Princeton, UCSD.
- Funding: $3.3M pre-seed from Lerer Hippeau, K5 Global, Exceptional Capital, Sabrina Hahn, Rohan Shah

## THE PROBLEM

Every AI tool today hits the same wall: they can't reliably access your company's knowledge. Claude Code can't query your Snowflake out-of-the-box. ChatGPT doesn't know your weird custom Salesforce schema. They hallucinate because they lack structured context.

The root cause: data infrastructure was built for humans, not AI agents reasoning about business context without tribal knowledge & context. Database access patterns are shifting. SQL won't be how AI systems query data in five years. We're moving to semantic queries, knowledge graphs, self-healing data models. No one has solved this.

You'll build the structured context management layer that makes AI context selection reliable in production. This isn't better RAG or fine-tuning. This is inventing new data access patterns and context architectures that power the next generation of AI applications in the fastest changing space around.

## WHAT YOU'LL BUILD

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

## About deepline.com

## Company Overview
- **One-liner**: Deepline provides a headless, programmable GTM (go-to-market) data layer that lets AI agents and developers unify enrichment, validation, CRM updates, and workflow execution through a CLI, API, and MCP server.
- **Entity Type**: Private (pre-seed, $3.3M raised)
- **Headquarters**: New York City, USA
- **Founded**: Not publicly available
- **Founders**: Jai, Saf, and Chirag (full names not publicly listed)

## Core Business
- Primary industry: GTM data infrastructure / Sales & Marketing technology (with strong AI integration)
- Target customers: GTM engineers, RevOps teams, developers, and AI agents at B2B companies
- Mission: Turn operator intent into governed execution and measurable outcomes — “ambient automation” that solves problems before they’re known.

## Products & Services
- **[Deepline CLI]**: Command-line tool for running enrichment, validation, CRM updates, and audience sync directly from the terminal.
- **[Deepline Runtime API]**: REST API for programmatic execution of GTM workflows, usable by scripts or AI agents.
- **[Remote MCP Server]**: Model Context Protocol endpoint that allows AI coding assistants (e.g., Claude Code, Codex) to discover and run Deepline tools.
- **[Plays]**: Versioned, repeatable GTM workflows that define tasks, inputs, provider calls, and output rules — turning manual processes into operational code.

## Market Standing
- **Valuation/Market Cap**: Not disclosed
- **Total Funding**: $3.3M pre-seed (date not specified)
- **Key Metric**: Connects 76+ data providers for sales, marketing, and RevOps teams
- **Notable Investors/Partners**: Lerer Hippeau, K5 Global, Exceptional Capital, Sabrina Hahn, Rohan Shah
- **Growth Signals**: Proven product-market fit with growing adoption; small senior team from Uber, Lyft, OM1, Capchase; hiring across engineering, design, GTM engineering, and partnerships.

## Competitive Advantages
- **Headless & programmable** – no visual spreadsheet, pure CLI/API approach appeals to developers and AI agents.
- **Unified provider layer** – single interface to 76+ data sources, eliminating the need for multiple API integrations.
- **Agent-native architecture** – designed from the ground up for AI agents to discover, invoke, and inspect workflows via MCP.
- **Transparent execution** – every run record is inspectable, making workflows auditable and repeatable.

## Strategic Focus
- Building a **context management API** that gives AI systems reliable structured context, replacing brittle RAG or fine-tuning.
- Developing **new data access patterns** (semantic query interfaces) that replace SQL for AI agents.
- Pushing toward **ambient automation** — autonomous GTM execution with guardrails and observability.
- Scaling the team (currently hiring engineer #5-6) and deepening enterprise integrations (Snowflake, Salesforce, Gong, etc.).

## Why Work Here
- **Tiny senior team** – work directly with founders and a handful of engineers from MIT, Waterloo, Berkeley, Princeton, UCSD, and previous startups (Uber, Lyft, OM1, Capchase).
- **Fast iteration** – ship multiple times a day, small reversible changes, no quarterly launches.
- **In-person culture** – strong NYC hub with quarterly off-sites; remote-friendly but in-person preferred.
- **Compensation** – $140K–220K base + meaningful equity for early-stage roles.
- **High ownership** – as one of the first engineers, you’ll define the architecture and data patterns for the next generation of AI-powered GTM.
- **Technical challenges** – build semantic layers, knowledge graphs, self-healing data models, and multi-agent orchestration systems.

## Sources
1. [deepline.com](https://deepline.com/)
2. [deepline.com/about](https://deepline.com/about)
3. [deepline.com/blog/what-is-deepline](https://deepline.com/blog/what-is-deepline)
4. [linkedin.com/company/deeplinedata](https://www.linkedin.com/company/deeplinedata)
5. [jobs.gem.com (Deepline careers)](https://jobs.gem.com/deepline-com) (scraped via search results)

## Other roles at deepline.com

- [Founding Customer Success Engineer](https://feeny.ai/job/founding-customer-success-engineer-deepline-com-new-york-zrjb5h0de1t9) — New York, NY
- [Founding Backend Engineer](https://feeny.ai/job/founding-backend-engineer-deepline-com-new-york-hgh43kdexaf8) — New York, NY
- [Founding Go-to-Market Lead](https://feeny.ai/job/founding-go-to-market-lead-deepline-com-new-york-xqgqd5yz8n82) — New York, NY
- [Founding Head of Growth](https://feeny.ai/job/founding-head-of-growth-deepline-com-new-york-h8tazkzcawgb) — New York, NY
- [Founding Design Engineer](https://feeny.ai/job/founding-design-engineer-deepline-com-new-york-9wqv76e3v6hy) — New York, NY
- [Founding Full-Stack Engineer](https://feeny.ai/job/founding-full-stack-engineer-deepline-com-new-york-mv35r4szt57c) — New York, NY