Slai is a tool to quickly build machine learning-powered applications. Our browser-based sandbox is the easiest way to build, deploy, and share machine learning models with zero setup. Here’s an example app on our platform.

Dozens of data teams are using Slai to power their machine learning models in production. And we have a thriving community of developers using our platform to build apps using state-of-the-art models, like Stable Diffusion and GPT-2.

We’re looking to bring someone on to help us with SRE / DevOps work. If you like working on distributed systems and machine learning at scale, we have the role for you. Our system is used for rapidly creating serverless runtimes to run our customers' ML training code and autoscaling their deployments according to traffic. You'll be responsible for helping to scale our infrastructure to support the growth of our customers. Since we host ML models for customers, we grow when they do -- and boy, are some of them growing fast.

Engineers on the team

Work with Python, Django, Kubernetes, React, Redis, and Postgres
Spend time directly interacting with customers to understand their problems
Share frequent and honest feedback with each other
Fork open source libraries to solve challenges unique to our business
Help guide product decisions and high-level strategy

Skills & Experience

3+ years of experience building and maintaining infrastructure at scale
Fluent with Docker/Kubernetes, EKS, KNative, Terraform, Terragrunt, Gunicorn, and Python
Excellent writing and communication skill - can distill complex topics into bullet points
Enthusiasm for AI / ML, and open source software

Benefits

Competitive salary and meaningful equity
Flexible work environment – work remotely, or from our HQ in Cambridge, MA
Health, dental, and vision benefits with 90% coverage for you and 50% for dependents
Weekly all-hands meetings each Friday
Quarterly in-person trips
Minimal meetings and obstructions -- we value the flow state
Collaborative and transparent work environment -- we openly share our wins and our challenges and work through problems as a team
Fitness stipend
Much, much more

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