Beam
Ultrafast Inference for AI Products
Software Engineer, Growth
About the role
Beam helps developers build and run AI and LLM applications on serverless GPUs, without managing infrastructure. Hundreds of teams are using Beam to power their applications in production. And we have a thriving community of developers using our platform to build state-of-the-art apps with AI.
We’re looking to hire a Growth Engineer to join our small team. This role sits at the intersection of AI engineering, content marketing, and memes. You will find trends, build cool things, and ship fast. Here’s what you’ll work on:
- Build demo apps. Build and launch mini apps and demos that show what our product can be used for
- Create Technical Assets. Craft sharable assets, like product videos, screenshots, and mini case studies
- Ship Across Channels. Launch your work across channels like Twitter, Reddit, and Hacker News
Your experience
- Full-stack software engineering experience
- Previous experience shipping a product from 0 → 1, as a founder or an indie hacker
- Tuned into internet culture: you know what’s working on Twitter, and you know what’s played out
- Able to work independently and make fast decisions under ambiguity
- Excited to collaborate with customers
- Enthusiasm for developer tools, cloud native technologies, and open source software
Benefits
- Competitive salary with meaningful equity
- Health, dental, vision, 401(k)
- Join a fast-growing pre-series A company at the ground floor
- Fitness stipend, learning budget, and much more
About Beam
Beam is a tool to quickly build machine learning-powered applications. Our platform helps developers run their code on serverless GPUs, deploy highly performant APIs, and rapidly prototype ML models — without managing any infrastructure.
Machine learning is eating software, but it’s still difficult for developers to leverage ML in their products. Today, companies are spending months building their own ML platforms, or relying on outdated tools that were originally designed for academics.
We believe that for ML to reach widespread adoption, the underlying infrastructure needs to be hidden from the user. We're building the fastest way for developers to go from an ML prototype to a production service.
