Software Engineer - Forecasting & Scheduling

assembledhq; United States; On-site; USD 80,000 - 120,000; Full time

Job summary

Assembled in the United States is seeking a talented individual to enhance their customer support platform through machine learning. The role involves developing forecasting models to predict support contact volumes, designing optimal scheduling systems for thousands of agents, and improving the efficiency of machine learning operations.

The ideal candidate will possess strong skills in Python and experience working in algorithmic teams. Assembled values diverse perspectives and is committed to an inclusive workplace, encouraging all qualified individuals to apply for the role.

Qualifications

• Experience using Python libraries like pandas, SciPy, and seaborn for statistical or predictive work.
• Previous experience working on a machine learning or algorithmic team.
• Strong commitment to advancing statistical and runtime performance.

Responsibilities

• Develop forecasting interfaces, data pipelines, and inference servers to predict support contact volume.
• Design and implement scheduling interfaces for support agents based on forecasts and constraints.
• Enhance machine learning efficiency and operations to support rapid model deployment.

Skills

Python libraries

Data analysis

Machine learning

Statistical performance

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

• 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.
• 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.

About you (specifically)

• 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.

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.