Assembled builds the infrastructure that underpins exceptional customer support, empowering companies like CashApp, Etsy, and Robinhood to deliver faster, better service at scale. With solutions for workforce management, BPO collaboration, and AI-powered issue resolution, Assembled simplifies the complexities of modern support operations by uniting in-house, outsourced, and AI-powered agents in a single operating system. Backed by $70M in funding from NEA, Emergence Capital, and Stripe, and driven by a team of experts passionate about problem-solving, we’re at the forefront of support operations technology.
What you’ll work onPredicting contact volume: Developing forecasting interfaces, data pipelines, and inference servers to predict support contact volume and determine the optimal number of support agents required for specific days and times.
Scheduling 1000s of support agents: Designing and implementing interfaces to collect and store team preferences and customer business constraints (e.g., labor laws), enabling the creation of optimal schedules for teams of thousands of support agents based on these forecasts and constraints. (check out https://en.wikipedia.org/wiki/Nurse_scheduling_problem)
MLOps: Enhancing machine learning efficiency and operations to support rapid model deployment and iteration.
Experience with translatable languages: Extensive back-end engineering experience in statically typed languages like Go, Java, or Rust.
Familiarity with ML packages and software: Experience using Python libraries like pandas, SciPy, and seaborn for statistical or predictive work.
Background in ML or algorithmic teams: Previous experience working on a machine learning or algorithmic team.
Passion for performance: A strong commitment to advancing both statistical and runtime performance, ensuring reliable and efficient forecasting and scheduling.
