---
title: 'Founding UX/Design Engineer at Deepline'
canonical: 'https://feeny.ai/job/founding-ux-design-engineer-deepline-new-york-hf17y78kyz05'
type: 'job'
last_seen: '2026-09-05'
---

# Founding UX/Design Engineer at Deepline

- **Company:** Deepline
- **Location:** New York, NY
- **Employment:** full-time
- **Work type:** onsite
- **Posted:** 2025-11-17
- **Last confirmed live:** 2026-09-05
- **Apply:** https://jobs.gem.com/getaero-io/am9icG9zdDrX7s_daKr0V2WUOy-V6iFT

## Job description

## About Deepline

Deepline is the deep research platform for businesses.

While AI has changed the way we access and utilize information, it has yet to do so for enterprise. The reason: necessary business context is hiding in prohibitively messy, disjoint datasets - strewn across disconnected systems, unlabeled, painful to access for humans much less LLMs.

The best orgs build a team of data experts to wrangle this context – we’re replacing this need.

We’re a small team bringing decades of data experience from Uber, Lyft, and MIT. We’re dedicated to truthseeking conversation, shipping speed, and building magical user experiences.

## Role

We’re seeking a Founding Design Engineer to help design the future of human-process interaction.

You’ll work closely with our founders to design new iterations of our product and ship fast.

Expect strong ownership, spirited debate, and novel technical challenges at the frontier of

- multi-agent systems
- A.I. powered user interfaces (chat, app builders, workflows)
- reinforcement learning with human feedback
- semantic layers
- unstructured data processing
- knowledge graphs/ontologies

## What you’ll do

- Shape and ship new product capabilities end-to-end alongside our team
- Engineer context and tools for our AI systems. Our team has solved access to clean datasets – now comes the [delicate science](https://x.com/karpathy/status/1937902205765607626) of feeding this data into LLM context.
- Design for an intimidatingly broad set of use cases: our customers have incredibly open-ended questions, tactical and strategic - how do I increase revenue? Which customers are struggling with my product? Which competitors am I losing deals to? What should I do about this?

## Why join now

- Building Business Logic Compilers: We’re building for the shift towards business logic abstractions. The growth potential is immense; and we’re building a team obsessed with execution to get us there.
- Make Asymmetric Bets alongside Brilliant People: Our founding team are domain experts on these problems, coming from Uber, Lyft, and MIT to top tier enterprises. Joining early means real ownership in the upside you help create.
- Forget Incremental Improvements: You’ll be building frontier multi-agent systems tackling engineering challenges that could fundamentally transform how knowledge work is done.

## What we’re looking for

You may be a fit if you…

- Strong attention to detail & high-bar
- Are a self-starter, and have a track record of building products from 0 → 1
- Enjoy both design & engineering craftsmanship - powerful/scalable systems that support delightful UX
- Savor critical feedback from customers as growth opportunities
- Embrace the latest AI dev tools (e.g. Cursor, Claude Code) to build better and faster

We’re excited to work with engineers who’ve built full-stack, multi-cloud systems and are quick to learn new tools. Our stack is Typescript/Vercel, Python (Pydantic/FastAPI), SQL, DBT, and LLMs/multi-agent systems galore.

Applying Curious? We’d love to chat - send a resume/linkedin and a note to team@deepline.com

## About Deepline

## Company Overview
- **One-liner**: Deepline is a headless GTM (go-to-market) data platform that gives AI agents and operations teams a unified CLI, Runtime API, and remote MCP server to enrich, validate, deduplicate, and sequence data across 76+ (and growing) sales, marketing, and RevOps providers with a single command.
- **Entity Type**: Private (Pre-seed; operates as Aero AI Labs, Inc.)
- **Headquarters**: New York, NY, United States (strong NYC hub, in-person roles with quarterly off-sites)
- **Founded**: Not publicly available in current sources
- **Founders**: "Jai, Saf, & Chirag" — first names only per company materials; full names not publicly listed. Founding team previously at Uber, Lyft, OM1, and Capchase (MIT, Waterloo, Berkeley, Princeton, UCSD).

## Core Business
- **Primary industry**: Go-to-market data infrastructure / AI agent tooling (sales intelligence, marketing operations, RevOps automation)
- **Target customers**: B2B GTM teams, RevOps leaders, forward-deployed engineers, and high-complexity enterprises; also developers building AI agents that need structured business context
- **Mission**: Turn operator intent into governed execution and measurable outcomes — an "operating system for GTM execution" with a vision of "ambient automation that exists & solves problems before you know they exist."

## Products & Services
- **Deepline CLI**: A command-line interface that lets humans or AI agents discover data tools, run enrichment and validation, route results into CRM/sequencing systems, and inspect run history. Built to be used in public, with visible diffs.
- **Runtime API**: An HTTP surface for programmatic GTM work, with typed input/output schemas per provider-backed action. Designed to replace 20+ individual API calls to dozens of tools with a single call to Deepline's Context API.
- **Remote MCP Server**: A Streamable HTTP endpoint so MCP-capable AI agents (e.g., Claude Code) can authorize into the workspace and run the same GTM capabilities as a person or scheduled process.
- **Deepline Plays**: Reusable, inspectable workflow definitions that separate a GTM job into four layers — Tools (provider operations with defined schemas), Workflows/Plays (with branches, limits, output rules), Execution Interfaces (CLI/API/TS), and Run Records (status + results for audit and iteration). Plays replace one-off spreadsheets, browser tabs, and private prompts.
- **Context API (in development)**: A semantic context-management layer aiming to give AI systems structured, self-healing business context — including new data-access patterns that could replace SQL-style queries for AI agents, identity resolution across enterprise systems, and knowledge graphs that evolve with the business.

## Market Standing
- **Valuation**: Not disclosed
- **Total Funding**: $3.3M pre-seed (current stage), with "proven product-market fit and growing adoption" per company materials
- **Notable Investors/Partners**: Lerer Hippeau, K5 Global, Exceptional Capital, Sabrina Hahn, Rohan Shah
- **Growth Signals**:
  - Integration count grew from 40+ providers in the founding-engineer job post to "76+ data providers" on the careers page, to "89+ GTM integrations" on the homepage — indicating rapid connector expansion.
  - Hiring across engineering, design, GTM engineering, and partnerships; prospective hires would be engineer #5-6.
  - Multiple open NYC-based founding roles (Forward-Deployed Engineer, Full-Stack Engineer, UX/Design Engineer, GTM Engineer) listed on Gem-hosted careers page.
  - Product shipped across three interfaces (CLI, API, MCP server) with public documentation, OpenAPI metadata, MCP metadata, and an llms.txt index.

## Competitive Advantages
- **One unified layer for GTM data**: A single CLI/API that connects 76+ (up to 89+) providers for enrichment, validation, CRM updates, audience sync, and sequencer pushes — versus stitching together 20+ tool-specific APIs.
- **Agent-native by design**: Ships a remote MCP server out of the box, so AI agents can operate the same GTM stack as humans without custom integration work.
- **Durable, inspectable execution**: "Plays" and run records make repeatable GTM operations explicit, auditable, and re-runnable — a governance layer that most GTM automation tools lack.
- **Semantic-layer ambition**: Investing in context management, self-healing data models, and knowledge graphs — positioning as infrastructure, not just another point automation tool. Company states this is "not better RAG or fine-tuning" but a new data-access paradigm for AI.
- **Senior, operator-focused team**: Small team with backgrounds from Uber, Lyft, OM1, Capchase and top CS programs; first-principles culture with daily shipping.

## Strategic Focus
- **Build the universal API/context layer for B2B businesses**: Make AI agents reliably able to purchase/access proprietary data, query internal knowledge bases, and act on business context without hallucination.
- **Pioneer new data-access patterns**: Develop semantic query interfaces that could replace SQL for AI agents; build retrieval pipelines that reason about context before querying.
- **Enterprise go-to-market**: Forward-deployed engineering model — embedding with high-complexity customers to deploy Deepline into CRMs, enrichment stacks, and sequencing tools, and proving value live on sales calls.
- **Self-healing infrastructure**: Architect feedback loops so data models and knowledge graphs improve from usage, detect broken context, and fix it automatically.

## Why Work Here
- **Early-stage ownership**: New hires join as engineer #5-6 with direct collaboration with founders and customers; every role is a "founding" role with broad scope.
- **Compensation**: $140K–220K base + meaningful equity for engineering roles (per Founding Full-Stack Engineer posting); contract-to-hire options for some roles.
- **Work environment**: In-person in NYC with quarterly off-sites; company describes itself as "remote-friendly with a strong NYC hub" but current open roles are listed as In Office / In-Person (NYC).
- **Engineering culture**: Ship multiple times per day, small reversible changes over quarterly launches; "first-principles debate" encouraged; docs, changelogs, and eval results written for the reader; decisions made by the people closest to the GTM problem ("no theatrical reviews").
- **Direct hiring process**: Apply directly through the Gem-hosted careers page — no recruiter middleman.
- **High-intensity, customer-facing work**: Forward-deployed roles involve live customer sessions, building solutions on sales calls, and seeing production impact at scale — suited to engineers who enjoy visible, immediate results.

## Sources
1. [deepline.com/careers](https://deepline.com/careers)
2. [deepline.com/about](https://deepline.com/about)
3. [deepline.com](https://deepline.com/)
4. [tealhq.com](https://www.tealhq.com/job/founding-gtm-engineer_7ea1aea54f6ffdaff441031e8569cca2d193c)
5. [deepline.com/blog/what-is-deepline](https://deepline.com/blog/what-is-deepline)

## Other roles at Deepline

- [Founding Context Engineer](https://feeny.ai/job/founding-context-engineer-deepline-new-york-j94wqavx1179) — New York, NY