We are on a mission to create the forecasting foundation model to rule them all. Forecasting drives critical decisions worldwide - impacting staffing, supply chain management, finance and more. Our solution provides companies with the models, platform and APIs they need to easily generate the most accurate forecasts possible, helping to significantly reduce waste and enabling smarter, more confident decisions.

Who we’re looking for

The forecasting model is at the heart of our technology. As the second founding MLE, you will build, train and deploy large foundation model architectures: implement and combine ideas from the literature, push the state of the art, and ultimately deploy your model for our customers to use in production. Our goal is for our models to be the best for our customers’ use cases - including for capabilities that do not exist yet in academic models.

You love your craft, have high standards, stay up-to-date with the latest ideas in ML, and know when to make trade-offs to ship. You live and breathe neural networks, and speak PyTorch or Jax. You are used to diving deep in large amounts of data, and you know what you train your models on. Bonus if you have experience building solid ML infrastructure.

You are passionate about your craft, maintain high standards, stay current with the latest tech and know when to make trade-offs to deliver results efficiently. We do not believe great engineers are “jack of all trades”, but rather that they excel at diving deep into complex topics quickly, leveraging a broad range of experiences to solve challenging problems. You are also open to exploring new concepts, technologies, and enjoy quickly throwing prototypes together to kick the tires. You prefer quick feedback loops, rather than aiming for perfection on the first try.

What you’ll be doing

• Architect and train time-series foundation models using diverse datasets, integrating multimodal inputs like numerical time series, text, location and image data