Beam
AI Infrastructure for Developers
Software Engineer
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 someone to help us with Platform Engineering work. We’re working on lots of fun problems:
- Low-level systems development
- GPU memory sharing
- Cloud development environments that feel just like working on your laptop
Our backend is mostly in Go, with some Python. We try to keep distractions to a minimum, we have quarterly hackathons at our office, and we value getting into a flow state at work.
We're a small team doing the most impactful work of our careers. This is a career-defining role but it does come at a cost -- we cannot promise exceptional work-life balance at this stage of the company. If this trade-off is acceptable, we invite you to continue reading.
Skills & Experience
- 3-5 years of experience working with a large distributed system
- Comfortable with Kubernetes
- Experience writing a statically typed language, like Golang or Rust
- Familiarity with things like gRPC, Helm/Kustomize, and Terraform
- Enthusiasm for dev tools, cloud native technologies, ML, and open source in general
Benefits
- Competitive salary and meaningful equity
- Join a fast-growing pre-series A company at the ground floor
- Health, dental, and vision benefits with 90% coverage for you and 50% for dependents
- Opportunities to participate in events across the cloud native community
- Fitness stipend, learning budget, and much, 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 Today, companies are spending months building their own ML platforms, or relying on outdated tools that were originally designed for academics.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.
