Job Description

Slack is looking for a Senior Software Engineer with strong exposure to data to support the development of Machine learning models that leverage our data to make Slack a fabulous, robust, safe, and valuable product for our users. You will be working on creating large-scale data pipelines, continuous training / deployment, and enhancing our experimentation capabilities, as well as working alongside both our Machine Learning modeling and infrastructure teams to help deliver ML features across Slack. At Slack, your impact can be huge: Here at Slack, we believe we can build terrific product experiences for our customers with ML + AI through letting users tap into their organizations’ collective knowledge. We have an opportunity to develop experiences that automate mundane tasks, efficiently find answers, and sift through the massive amount of information at a company to find what’s relevant for a particular worker. We’re investing in this area in a drive to make the work lives of the millions of knowledge workers who rely on Slack day to day more productive and delightful. 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. We operate at significant scale and our new ML platform group has significant opportunity to help better deliver ML capabilities across Slack. Our platform group is responsible for supporting data ingestion and transformation, feature creation and management, as well as ensuring that we have robust experimentation capabilities both offline and online. We are looking for highly talented engineers to help deliver across these objectives. What you will be doing: Develop software across the ML ops stack (e.g., data ingestion / processing, management of ML models, expand experimentation capabilities, etc.) Brainstorm with ML, infrastructure, and product engineers to deliver highly efficient and robust capabilities around creating and delivering ML models. Optimize machine learning pipelines for scalability, efficiency and cost-effectiveness. Support in the creation and management of ML features. Actively own features or systems and define their long-term health, while also improving the health of surrounding systems. Assist our skilled support team and operations team in triaging and resolving production issues. Mentor other engineers and deeply review code. Improve engineering standards, tooling, and processes. What you should have: Experience with functional or imperative programming languages: PHP, Python, Ruby, Go, C, Scala or Java. Experience building big data processing pipelines with tools like Apache Spark, Hadoop, PySpark, SQL, etc. Experience orchestrating complex data ingestion and processing pipelines with tools like Airflow, Dagster, or Luigi. Have built large-scale, distributed, production ML/AI systems or other ML Ops systems professionally and can point to things you’ve worked on. An analytical and data driven mindset, and know how to measure success with complicated ML/AI products. Put machine learning models or other data-derived artifacts into production at scale. Led technical architecture discussions and helped drive technical decisions within the team. The ability to write understandable, testable code with an eye towards maintainability. Strong communication skills and you are capable of explaining complex technical concepts to designers, support, and other specialists. Strong computer science fundamentals: data structures, algorithms, programming languages, distributed systems, and information retrieval. #J-18808-Ljbffr Salesforce, Inc..

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