AI Search Engineer

Location

In-Person (3 days a week), NYC

Compensation

Salary ($160 - $220K) + 0.3-0.5% Stock Option

Description

Shade is scaling and fast. In a year and half, we’ve built out the combined tech of FrameIO (acq by Adobe for $1.275B) and LucidLink ($40M ARR) while combining it with proprietary AI search/labeling. We handle thousands of hours of video and tens of millions of requests every day, and we’re a critical piece of infrastructure for post-production houses, creative agencies, sports teams, and internal media teams at large companies. Customers include Salesforce, Snowflake, Grüns, Hello Fresh, Deloitte, Motorola, Stagwell Media Group, and Lennar. We’re growing 150% QoQ, 120% NRR.

Backed by Khosla, General Catalyst, Contrary, Signalfire and Bling

We’re not done yet—rather, we’re just getting started. We’re building the next version of Shade to be the platform solving pain points in storage systems that aren’t even being addressed yet. This includes

• Build the smartest search engine that will index all of the files in the world

  • The amount of data stored in files, media, and documents is enormous. We want to build the best multi-modal search engine across metadata, chunks, LLMs and more. People want to find exactly what they’re looking, synthesize these results for projects and building the “digital librarian” for their file system.

• Data transfer is unsolved

  • From hot storage → archive storage, cloud → cloud, camera → editor, moving high volumes of data is still flaky, unreliable and difficult. We are building the tooling and UI directly into our platform to make this seamless at scale.

• Version control is useful for everyone

  • You’re an engineer - git history is useful. We’ve built git for creatives because the same concepts are useful for media teams. We save every version and every file as a commit in our database as changes are made. We have the backend built but we need to build the git UI for creatives.

• Integrate with everything

  • Project management tools, AI tools, ad generators and everything in between. Someone has to store the data when its moved between platforms. We want to be that layer.

Shade is built on Python, NodeJS, NextJS, and C++ with a postgres database.

Our core tenets for design are

• Keep dependencies as minimal as possible

  • You are the summation of your subprocessor’s/dependencies issues. To build a durable and reliable company you must be deliberate when you add dependencies and control the vision of all the code you ship.

• Monolith > microservices. Transactional everything requires one database.

• Solve the core issue. Don’t invent a bandaid

  • Ex: if a database query is slow and address it directly rather than reaching for a cache.

• The simplicity of fs.readFile() always wins

  • Have you tried to access files in a dropbox local drive in your programs? It doesn’t work. Files must be manually downloaded in their entirety to be accessed. We’ve built Shade to be accessible like a hard drive where files are streamed. Building an AI video editor? Works with Shade. Using n8n automations? Works with Shade. Using Davinci resolve? Works with Shade.

Our core tenets for the team are

• When we hire we like to keep those hires. Because of this we offer benefits on top of salary + equity

  • Free lunch (<$30)

  • Free dinner (<$30) if you stay more than 9 hours

  • Fully covered health insurance including dental and vision

  • 401k with % match

  • Unlimited PTO

  • Lifetime gym membership

  • Commuter benefit for subway

• Shipping code happens in person

Qualifications

The greatest qualification in our eyes is that you can ship and maintain high volumes of quality code. If you’ve built side projects that are used by thousands of people or worked at companies where you’ve owned features end to end then we’re probably excited about you. What (we think) this looks like in bullet points:

• 3+ years of full-time engineering experience

• Proven track record of owning AI search or information retrieval systems in production end to end

• Strong Python experience, including building and maintaining backend services and data pipelines

• Hands-on experience with LLM-powered search experiences, including retrieval-augmented generation (RAG), evaluation, and prompt and tool design, vector DB (pgvector)

• Metrics and analytics driven on AI and search performance

• Experience with vector databases and vector search (indexing, retrieval, filtering, hybrid search), and an understanding of embeddings and ranking

• Experience building and operating vector pipelines, including document chunking, metadata enrichment, backfills, and continuous re-indexing (Unstructured.IO, Chonkie)

• MCP server work is a bonus

• Experience integrating with RESTful backend services

• Good judgment about system architecture, developer experience, and where tooling and code quality need improvement

• Based in NYC

• Experience at a pre-Series B startup