At Weights & Biases, our mission is to build the best developer tools for machine learning. Weights & Biases is a series C company with $200 million in funding and a rapidly growing user base. Our platform is an essential piece of the daily work for machine learning engineers, from academic research institutions like FAIR and UC Berkeley to massive enterprise teams including iRobot, OpenAI, Toyota Research Institute, Samsung, NVIDIA, Salesforce, Blue Cross Blue Shield, Lyft, and more.

In this role, you will work on building out the backend to provide a reliable and resilient API for enterprise customers and self-contained private deployments. You’ll partner closely with sales, customers, and other engineering teams at W&B to ensure our

evolving enterprise customer needs are met and integrate well with our planned new features. You’ll be instrumental in building and writing code for distributed systems and establishing the patterns and practices necessary to operate highly reliable services at scale.

What you’ll achieve

• • Build a monitoring and observability platform to pinpoint issues across a fleet of customer deployments.
  • Join a highly cross-functional team, interacting with different teams from engineering, to sales.
  • Work with our backend and infrastructure team to design and develop resilient, highly available systems with data privacy in mind
  • Advise and educate development teams on how to build observable, reliable services.

What’s needed in this role

• • A willingness to dive into and debug issues at any layer of the tech stack, from the application layer to the network.
  • In-depth knowledge of at least one cloud provider (AWS, GCP, Azure).
  • Strong grasp of Golang and its ecosystem
  • Experience using project management software, specifically Jira
  • Experience building and maintaining public APIs
  • Deep experience managing, monitoring, and debugging distributed systems/databases (MySQL, Postgres, etc.) in production.
  • A critical eye for detail to ensure bugs are caught and fixed in the staging environment