Founding Go-to-Market Engineer (Contract-to-Hire)
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Before you apply
Salary$140k – $260k
LocationNYC, New York, United States
Work modeon-site
Typefull-time
SenioritySenior
Experience3+ yrs
DepartmentTechnology
EquityIncluded
Company size11–50 people
PostedMar 17, 2026 · 7mo ago
Verified live1d ago
May no longer be actively hiring
Posted by the company on Mar 17, 202671% of this company's open roles have been listed for 90+ days
At a glanceSummarised by Seekless from the posting.
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
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
Skills
Python
TypeScript
JavaScript
SQL
Snowflake
BigQuery
Redshift
dbt
Kafka
Pulsar
Hightouch
Census
Salesforce
HubSpot
Segment
Gong
Slack
Zendesk
Mixpanel
Amplitude
LangChain
LlamaIndex
LangGraph
CrewAI
Airflow
Prefect
Cube
Anthropic Claude API
OpenAI API
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.
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.
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Team: Small senior team from Uber, Lyft, OM1, Capchase. MIT, Waterloo, Berkeley, Princeton, UCSD.
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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.
Responsibilities
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
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.