Software Engineer - Forecasting & Scheduling
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At a glance
Job overview
Assembled is hiring a Software Engineer - Forecasting & Scheduling. Assembled seeks a Software Engineer to develop forecasting interfaces, build data pipelines, and create inference servers that predict support contact volume. The role also involves designing scheduling systems for thousands of agents, incorporating team preferences and labor constraints, and improving MLOps for rapid model deployment and iteration.
Key focus areas include Predict contact volume by developing forecasting interfaces, data pipelines, and inference servers to estimate support demand., Design and implement scheduling interfaces that collect preferences and constraints to create optimal schedules for thousands of agents., and Enhance MLOps to improve machine learning efficiency and support rapid model deployment and iteration..
Preferred (not required): Python, Pandas, SciPy, and Seaborn.
Skills & qualifications
Skills
Qualifications
Full job description
About Assembled Great customer support requires human agents and AI in perfect balance, and Assembled is the only unified platform that orchestrates both at scale. Companies like Canva, Etsy, and Robinhood use Assembled to coordinate their entire support operation — in-house agents, BPOs, and AI — in a single operating system. With AI Agents that resolve cases end-to-end, AI Copilot for agent assistance, and AI-powered workforce management that optimizes both human and AI capacity, Assembled helps teams deliver faster, better service while making smarter decisions about how to staff and automate. Backed by $71M from NEA, Emergence Capital, and Stripe, we're building the platform that makes AI and human collaboration actually work.
What you’ll work on
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Predicting 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.
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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 )
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MLOps: Enhancing machine learning efficiency and operations to support rapid model deployment and iteration.
About you (specifically)
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Familiarity with ML packages and software: Experience using Python libraries like pandas, SciPy, and seaborn for statistical or predictive work.
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Background in ML or algorithmic teams: Previous experience working on a machine learning or algorithmic team.
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Passion for performance: A strong commitment to advancing both statistical and runtime performance, ensuring reliable and efficient forecasting and scheduling.
We know great candidates don’t always meet every requirement listed in a job description. If the role excites you and you believe you can make an impact at Assembled, we encourage you to apply. We value diverse perspectives and are committed to building an inclusive workplace where everyone feels like they belong and has the opportunity to do their best work. We look forward to hearing from you!
For United States Applicants: Assembled participates in E-Verify and will provide the federal government with your Form I-9 information to confirm that you are authorized to work in the United States.
For United Kingdom Applicants: Assembled is required to verify your right to work in the UK and will conduct a Right to Work check prior to employment in accordance with applicable law.
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