Engineering Manager - Forecasting & Scheduling

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.

Something interesting is happening. Right when everyone thought customer support jobs were being automated away, they’re starting to come back:

Businesses are starting to realize that the future is a combination of humans and AI working in harmony: AI handling technical questions and responses while humans cultivate relationships and create empathy.

Assembled is building the future of customer support operations in the AI-era. Our job is to make sure AI handles what it's best at, and to set up human support teams to capitalize on the things only people can do.

We're opening an Engineering Manager role on our Forecasting and Scheduling team: a key team pushing this hybrid world forward. Forecasting and Scheduling is a fullstack product team that builds the algorithms and user experiences that help support orgs get the right people working at the right times. You'll have wide autonomy to set technical direction and make product decisions, partnering closely with PM, design, ML engineers, and our GTM team to figure out what to build. You’ll also work directly with customers to make sure it actually changes how they run their support orgs.

• A chance to reimagine Workforce Management for the AI era. AI agents and humans now share the same workforce. How do we set humans up to do the most human parts of the job? This team gets to define the answer.

• Math meets craft. Forecasting and Scheduling spans deep technical work (NP-hard constraint problems, forecasting models, etc.) and the product surfaces customers touch every day. It's an unusually creative and high-leverage combination. It’s a team with a mix of operations research and software engineering backgrounds.

• At the core of Assembled. Forecasting and Scheduling powers one of the most impactful parts of our product. The team's output directly shapes how tens of thousands of agents get staffed and how hundreds of millions of dollars in support spend get allocated.

• Technical leadership and people leadership. The role spans a combination of hard technical problems (how do we make schedule generation 10x faster?) and the human side of running a sizable team in flow.

• T-shaped leader. You usually working at a very broad level and understanding the high level picture of the team and how the team fits into company level goals, but can dive extremely deep at any given time when necessary.

• A great team builder: You can internally to make the team feel excited about the future as well as being able to recruit high quality engineers to join. You should be able to pitch the vision of the team effectively and get people to buy into that vision.

• Holds the bar high. You hold both yourself and the team accountable: ensuring we are delivering value to our customers quickly and effectively.

• Champions technical investment. You are the one beating the drum for paying down debt, modernizing infrastructure, and raising the engineering bar. You also know when taking on debt is the right call to move fast, and can credibly tell those moments apart.

• Strong product instincts. You know what to build, not just how to build it. You can wear the PM hat in moments when the team needs it, and can push back on a roadmap when something doesn't feel right.

• Thoughtful about AI as a force multiplier. You have a real point of view on where AI speeds the team up (codegen, prototyping, research synthesis, etc.) and where it just creates more cleanup later. You can deploy it in ways that compound, without opening the slop cannon.

• Nice to have: Background in operations research or an adjacent quantitative field. Optimization, queueing theory, simulation, applied math, or ML with a heavy optimization bent. You'll be making technical calls on territory where this background would be helpful.