Senior Software Engineer, Weights & Biases

• 4+ years of experience in software engineering
• Strong software engineering fundamentals and knowledge of at least 1 modern programming language (Python, Go, Typescript, GraphQL, etc)
• Strong empathy for ML practitioners with a keen interest in understanding and addressing their challenges
• A desire to be self-driven and find the path to greatest impact
• Nice to have: experience working with ML Engineers and building tools to allow them to easily connect with their compute

• As the software engineer in charge of Weights & Biases Sweeps product, you’ll build and maintain key features to allow users to effectively tune hyperparameters
• You’ll work on the next generation of sweeps, with a focus on allowing users to effectively distribute their hyperparameter tuning scripts across clusters and cloud infrastructure
• You’ll also contribute to the development of Weights & Biases Launch, an execution layer being actively developed on the Weights & Biases platform
• You will have the opportunity to design system architecture and APIs so users can easily send their workflows to the cloud, or local clusters
• Design and implement features to help users build and tune the best models by leveraging large scale compute
• Lead the maintenance of our existing Sweeps product and ensure a seamless transition to the next generation
• Contribute to the design and implementation to an execution platform built on Weights & Biases
• Focus on our end users and listen to customer feedback in order to deliver value

• 🏝️ Unlimited vacation time
• 🩺 100% Medical, Dental, and Vision for employees and Family Coverage
• 🏠 Remote first culture with in-office flexibility in San Francisco
• 💵 $500 home office budget with new high-powered laptop
• 🥇 Truly competitive salary and equity
• 🚼 12 weeks of Parental leave
• 📈 401(k)

Total funding: $250m

Weights & Biases (W&B) builds development tools for machine learning, helping businesses to create and use deep learning models. In order for deep learning to work, it’s necessary for companies to be able to monitor how the models are behaving as they are trained. With over 200,000 customers, W&B has differentiated itself from its competitors such as IBM Watson Studio and Valohai by focusing exclusively on machine learning practitioners and not on sales aspects of the technology, and by its competitive pricing.