Slack is looking for a Machine Learning Engineer to craft and implement features, services, API methods, and models to leverage our data to make Slack a fabulous, robust, safe, and valuable product for our users. We're looking for candidates with experience or interest in search / conversational agents, but ultimately are looking for engineers who can help drive impact with machine learning across the organization. At Slack, that impact can be huge: We have over 10 million daily active users relying on our product. At peak usage, a million messages a minute pass through Slack. During the week, our users spend over a billion minutes a day active in our product. Machine learning engineers at Slack touch a great variety of parts of our technical stack. At different points, you might find yourself building data pipelines, training search ranking models, fine tuning LLMs, implementing features in our application, or analyzing experiment data. We don’t expect everyone to be an expert in everything, but we are looking for candidates with experience in Machine Learning, a strength in at least a couple of these, and who are excited to learn the rest. This is a practical machine learning team, not a research team. Our goal is to deliver business value with machine learning and data in whatever form that takes. Sometimes that means bootstrapping something simple like a logistic regression and moving on. Other times that means developing sophisticated, finely tuned models and novel solutions to Slack’s unique problem space. We are looking for engineers who are driven by driving impact for our business, building great products for our customers, and delivering robust, reliable services with machine learning.