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 own building new products that connect various stages of the machine learning lifecycle. You will partner closely with both internal teams and enterprise customers to improve the velocity of ML workloads. This opportunity means directly working with state-of-the-art ML organizations; shipping features that will have a direct impact on users’ day-to-day lives. You will also tackle key problems in ML systems today such as dataset management, model understanding, and production orchestration. If you are interested in shaping the future of ML tooling, then this is the right role for you!
What you'll achieve
- Design and build new products that reshape how ML engineers interact with their datasets and models
- Scale APIs and systems to support enterprise-level ML workloads and datasets
- Implement automation to assist ML engineers in finding optimal hyperparameters or run an inference job when training is finished
- Focus on our end users and listen to customer feedback in order to deliver value
- Keep up with the latest trends in the ML world and leverage existing tools and frameworks whenever necessary
What's needed in this role
- Ability to synthesize complex requirements into concrete features and milestones
- Strong software engineering fundamentals and knowledge of at least 1 modern programming language (Java, Go, Python, C++, etc)
- Experience working with ML or data pipelines (Kubeflow, Spark, Sagemaker, etc)
- Basic understanding of ML algorithms or familiarity with some ML framework (PyTorch, Tensorflow, XGBoost, etc)
- Experience with cloud platforms and technologies (AWS, GCP, Kubernetes, etc)