Founding AI Engineering Lead in NYC

Along AI

New York (NY)

On-site

USD 130,000 - 160,000

Full time

14 days+

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Benefits offered by this job

Competitive base salary

Significant founding equity

25 days PTO

Job summary

Along AI is seeking a Founding AI Engineer in New York City to lead the development of an innovative context engine. You will be responsible for architecting solutions and directing AI coding teams. This position offers a competitive base salary, significant equity, and the opportunity to work closely with the founders. Ideal candidates will have hands-on experience with LLMs, a strong backend foundation, and at least 3-5 years in engineering roles. Full-time in-office presence is required.

Qualifications

3–5+ years engineering experience, ideally at an early-stage startup.
Comfortable directing AI coding agents and reviewing output at speed.
Experience with knowledge graphs, vector DBs, and retrieval systems.

Responsibilities

Shape what we build and create context engines.
Architect systems before implementation.
Design retrieval systems and structured context pipelines.

Skills

Systems thinker

Hands-on LLM experience

Directing AI coding agents

Strong backend foundation: Node.js/TypeScript

Experience with knowledge graphs

Tools

GraphRAG

AWS

GCP

Job description

Founding AI engineer in a leading tech role

Competitive base salary + significant equity stake

Building the context engine for the agentic era

AI-native: you orchestrate agents, you don't just write code

Start: ASAP

About

At Along AI, we are building the modern context engine, the persistent infrastructure layer that sits between an organization's knowledge and every AI system it uses.

As a Founding Engineer, you'll shape not just what we build, but how. We work in-office in New York City, move fast, and ship together.

The Problem

The bottleneck for AI in business was never intelligence. It was memory. It was context.

Teams copy-paste background into prompts, rebuild instructions for every tool, and watch agents go off the rails because they lack institutional knowledge. Today's AI stack has a gaping hole between raw data and capable models, no persistent understanding, no living memory that compounds over time. RAG is a runtime patch, not a solution.

Our Solution: the Context Engine

Along AI connects to your systems of record, extracts the knowledge that matters, and organizes it into Kontext Keys — secure, self-contained units of context for a specific use case. One API serves Claude, ChatGPT, your own agents, or any tool in your stack. The same living knowledge graph powers all of them.

Behind every Kontext Key is a GraphRAG-powered knowledge graph — not a flat vector database, but a structured representation of your organization's entities, relationships, and processes. Context that compounds with every interaction.

What AI-native engineering means here

Orchestration over implementation — you direct agent pipelines, pick the right models, architect systems. Writing from scratch is the exception.

Context engineering — you design retrieval systems, memory layers, and structured context pipelines.

Eval-driven — evals before features. AI output quality is an engineering problem.

Prompt as code — versioned, tested, treated with full engineering rigor.

What we're looking for