Overview of role:

SupportLogic is a well-funded and highly disruptive SaaS platform company that enables innovative companies like Nutanix, HPE Aruba Networks, Qlik and Databricks to transform their Voice of the Customer (VoC) programs and harness true customer sentiment signals in real-time to proactively improve customer relationships, products and operations while decreasing churn and top-line revenue leakage. We hire big picture thinkers who can simultaneously roll up their sleeves and deliver with measurable impact. We dream in years, plan in months and execute in days. Our culture is honest, fast paced, collaborative and very down to earth.

This role will need to overlap 3-5 hours/day with Pacific Standard Time.

How your work will support our growth:

The mission of the SupportLogic Machine Learning (ML) team is to create and leverage cutting-edge ML models, especially Large Language Models (LLMs) that can extract new signals from unstructured data and make insightful, actionable predictions for our customers. We are responsible for: Maximizing the value of SupportLogic to our customers by advancing the frontier of ML performance and Intellectual Property (IP). Ensuring ML models deliver consistent, predictable, and improving performance in production environments by working with backend engineering. Extracting maximum utility from our predictions for our end users and customers by collaborating with product design, UI, and customer-facing teams. We seek a Machine Learning Engineer interested in building models our customers can rely on for accurate predictions and reading of signals. You will be working in a fast-moving and growing company; our team is made of self starters who are curious about learning and using new technologies, systems, and processes.

The work you’ll do: Validate - Increase confidence of model rollouts by enriching and automating model validation prior to and immediately after deployment. Measure - Provide insight into accuracy and relevance of ML model predictions in production by measuring and monitoring model input and output data distributions, as well as user engagement/feedback on predictions. Automate - Incorporate user feedback/activity into new ML model training by automation of data collection, model retraining, model measurement, etc., towards a goal of continuous automated model retraining. Build - Provide internal tools or incorporate commercial tools (e.g., ClearML) into data scientist workflows for data analysis, feature generation, model development, etc., to boost ML team productivity. Collaborate - Bridge the gap between ML research and production-grade backend code by working with other engineering teams to integrate new ML models or APIs into production.