Full Stack Engineer

Roboflow is rapidly expanding our engineering team to address the groundswell of user and customer needs. Over 250,000 developers (spanning from students to individual hackers & hobbyists to startups to employees of some of the world’s biggest companies) have now used Roboflow to build computer vision projects. Soon, every developer will have computer vision as a tool in their toolbox. Roboflow will be for computer vision what Microsoft was for the PC and Google was for the Internet.

The Opportunity

We’re looking for strong technical generalists to contribute to our core product and help us build the foundation for our rapidly expanding company.

As an integral part of our early team, this role will inevitably involve wearing a lot of hats. Wide-ranging curiosity and enthusiasm for diving into abstract problems, coming up with good solutions, and seeing them through to completion is essential.

Our core belief is that computer vision is a foundational technology that is going to transform nearly every industry. This is an opportunity to shape how millions of developers will experience and use it for the first time. Your contribution will have a massive impact.

The Role

You'll be tasked with a wide range of projects. Each engineer has a realm they focus on, but we're still small enough that we don't have the luxury of deep specialization. We're looking for technical generalists that aren’t afraid to dive into a new stack or toolchain if the need arises (but JavaScript and Node.js make up the brunt of the existing codebase).

Most of the things we work on are parts of the core product (which is an end-to-end pipeline for building computer vision projects spanning from image ingestion to annotation to training and deployment) but from time to time we're also working on things like integrating marketing and sales tools, fighting fires, automating internal processes, and open source projects.

You'll have a wide degree of freedom to advocate for which projects you think should be highest priority and will contribute to our strategy decisions. If you need a rigid list of tasks spelled out in a multi-month roadmap, this role probably won't be a good fit.

The majority of our codebase is written in JavaScript, our machine learning and image processing pipeline is in Python. We run primarily on Firebase and GCS, with some machine learning infrastructure on AWS. We're increasingly using Docker (both internally and for customer facing products like our edge inference server). A lot of our code runs in the browser (including some Tensorflow JS) but we're also working on building APIs and client libraries in several languages.

You certainly don't need to be experienced in all of these areas; but should be excited to learn new skill sets as you need them. We also hope you'll bring some new knowledge and experiences you can share to help level-up the rest of the team.

We’re especially keen to add some rigor to our processes and build the foundation for rapidly scaling the engineering organization (for example: we currently have limited tests and are not using an opinionated front-end framework -- things that will need to change over the coming months in order to be able to seamlessly expand the team).

Example Projects

To give you an idea of what it will be like to work here, here are a few projects you might work on in your first few months:

• Creating a filtering interface so our users can mix and match their images based on metadata like the time of day they were captured, the GPS location, or custom tags they've applied.
• Expanding our annotation tool to support new functionality (like polygonal annotations image segmentation).
• Integrating the core app with external APIs for things like outsourcing annotation tasks and analyzing deployed model performance.
• Streamlining our onboarding flow.
• Deeper integrations with our other products like Roboflow Universe and our REST API.
• Next-generation model assisted labeling.
• Optimizing speed and scalability.

We're a tightly knit and highly technical team. As a startup, speed and decisiveness are important; we try to demonstrate that during the hiring process.

• Application screen
• Technical screen (via Triplebyte for most engineering roles)
• Zoom call with hiring manager
• Zoom call with founder
• Hacking together on something with the team for a few hours (where applicable)
• Culture discussion, explanation of benefits, alignment on offer with HR
• Written offer
• Offer acceptance
• On-prem onboarding where practicable

We aim to screen, interview, make a decision, send and finalize an offer within 2 weeks of application.