Software Engineer - Forecasting & Scheduling
Develop AI-driven forecasting and scheduling systems to optimize support agent allocation.
Contribute to Assembled's unified support platform by predicting contact volume, scheduling thousands of agents, and enhancing ML operations. Ensure efficient and reliable forecasting and scheduling for improved customer service.
Why This Role?
Be part of a team building the future of AI and human collaboration in customer support.
Key Responsibilities
- Develop and maintain data pipelines and inference servers for contact volume prediction.
- Design and implement interfaces to collect agent preferences and customer constraints for optimal scheduling.
- Improve machine learning efficiency and operations for rapid model deployment and iteration.
Requirements
- Proficiency in Python libraries such as pandas, SciPy, and seaborn for statistical and predictive tasks.
- Previous experience on a machine learning or algorithmic team.
- Strong commitment to advancing statistical and runtime performance.
Required Skills
Indonesia Context
- Working Hours Overlap:
- Minimal overlap — opposite hours
Keywords
View Original Description from Ashby Job Boards
Original description from Ashby Job Boards
ABOUT ASSEMBLED Great customer support requires human agents and AI in perfect balance, and Assembled https://www.assembled.com/customers 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.
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Market data & reports
Salary & skill-demand research built from our own listings data.
- Indonesia IT Jobs vs Global Remote (2026)Primary analysis of 2,049 listings: methodology, classification rules, downloadable datasets.
- AI-Skill Demand: Indonesia vs Global Remote (2026)10,000+ postings, taxonomy-first classifier, Wilson CIs, pre-registered before analysis.
- Indonesia Hiring Report: Tech vs Non-TechJob demand by field from aggregate open-job counts — never individual listings.
- Indonesia Salary BenchmarkAggregate salary ranges across roles, with open methodology and dataset.
- Remote Market Reports by RoleAuto-generated per role family — skills, seniority, companies, salary.
