Junior AI Search Engineer

In-person, Manhattan (5 days/week). Hiring ASAP.

Shade is scaling, fast. In a year and a half, we’ve built out the combined tech of Frame.io (acquired by Adobe for $1.275B) and LucidLink — combined with proprietary AI search and labeling. 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, A24, the Boston Celtics, HelloFresh, Deloitte, and Motorola. We’re already ingesting 20–30% of Dropbox’s daily data: 50TB a day, 10M minutes of video a month, hundreds of millions of assets. We’re growing 200% QoQ with 120% NRR.

We also just closed a $14M Series A, backed by Khosla, General Catalyst, Construct Capital, Contrary, SignalFire, and Bling.

How we think about Shade

• Search at petabyte scale is unsolved. People want a multi-modal search engine that finds what they mean (not what they typed) across vector, transcript, metadata, and folder structure. This is the layer you’d be building on.

• Data transfer is unsolved. From hot storage → archive storage, cloud → cloud, camera → editor, moving high volumes of data is still flaky and unreliable. We’re building the tooling and UI directly into the platform.

• Version control wasn’t built for creatives. Git history is useful for engineers; the same concepts apply to media teams. We’ve built the backend but the creative UI isn’t there yet.

• Storage layers don’t talk to other tools. Files move constantly between project management tools, AI tools, ad generators, MCP servers, and Premiere panels. We want to be the layer that stores and reconciles them.

Why this role exists

We have a strong foundation. What we need now is someone to grow into owning the search layer on top of it. You’ll be working directly alongside our engineering team, learning how production AI search systems are built and operated at real scale, and taking on ownership as you ramp up.

What you’ll work on

Search at Shade today is good at generic queries: “skiing down a hill,” “B-roll of someone with a laptop.” Where it falls short is on business-specific intent. Grüns, one of our customers, makes gummy vitamins — they want to find clips of gummies falling from the sky. Our vectors aren’t strong enough alone to retrieve that, and pure semantic search doesn’t get there.

You’ll work on the indexing and retrieval pipeline: helping improve LLM re-ranking, evaluating retrieval quality, and iterating on chunking and embedding strategies. Every architectural decision has a real cost consequence, and you’ll learn to think that way. The system is live and customers depend on it, so you’ll be doing real work on a real product from day one.

You’ll work on integrating: Customers have data sources and information outside of the platform. We want to play well with someone’s entire business.

Stack

Shade is built on Python, NodeJS, Next.js, and C++ with a Postgres database. This role lives in the Python backend, with pgvector for vector storage.

Our engineering tenets

• Keep dependencies as minimal as possible. You are the summation of your dependencies’ issues. Be deliberate when you add them.

• Monolith > microservices. Transactional everything requires one database.

• Solve the core issue. Don’t invent a band-aid. If a database query is slow, fix the query.

• The simplicity of fs.readFile() always wins. We’ve built Shade to be accessible like a hard drive where files are streamed — not downloaded first.

Benefits

When we hire, we like to keep those hires. On top of salary and equity:

• Free lunch (under $30)

• Free dinner (under $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 the subway

What we’re looking for

The greatest qualification in our eyes is that you care about writing quality code and getting better fast. If you’ve built side projects that real people use, shipped features in a production codebase, or done meaningful coursework or research in ML/search, we’re probably excited about you. In bullet points:

• 0–2 years of full-time engineering experience, or strong internship, research, or project work that shows you can ship production code

• Solid Python fundamentals — comfort reading and writing backend code, not just notebooks

• Some exposure to LLM-powered systems: RAG, embeddings, semantic search — whether through work, coursework, or a side project you’re proud of

• Curiosity about how retrieval systems work and why they fail

• Pre-Series B startup experience is a plus, but not required

Who’ll thrive here

The strongest pattern in our hiring has been people who’ve felt the file system pain firsthand. Brandon was a videographer; Emerson directed films before he wrote code. You don’t have to have a creative background, but you do have to care about users who would rather be making something than managing files.

The second pattern is a point of view. We don’t have product managers. That means engineers are in the room when decisions get made, and the people who do best here are the ones with opinions — who can advocate for an idea, push back when they disagree, and hear they’re wrong without taking it personally. You don’t need ten years of experience to have a point of view.

The third is comfort with not having all the answers. When you go online to figure out how to solve something at our scale, the answers usually aren’t there. The engineers who thrive here treat that as a feature, not a bug. If you’re early in your career and want an environment that will invest in you and give you real problems to work on, this is that place.