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 we build on Forecasting & SchedulingContact-volume forecasting: data pipelines, statistical/ML models and inference services that predict ticket volumes, agent demand and time to resolution.
Queueing simulation: realistic models of synchronous (phone, chat) and asynchronous (email, messaging) queues that forecast wait times, staffing demand considering clearing weekend backlogs while still receiving new tickets.
Scheduling tooling: a calendar-like UI that lets managers create and adjust rosters for thousands of agents while respecting preferences, labor laws and SLAs.
Agent empowerment: self-service pages for shift swaps, time-off requests and overtime management.
Lead the architecture and delivery of new ML features end-to-end: research → prototype → production.
Drive technical roadmaps, code reviews and design sessions to share your knowledge with the rest of the team.
Mentor engineers, unblock thorny problems and act as subject-matter expert for data science topics.
Collaborate with Product and Design to turn unclear customer problems into shippable solutions.
5+ years shipping production time-series forecasts or similar ML systems.
Proficient in a typed backend language (Go, Java or Rust) and comfortable with Python for research.
Experience owning services in AWS or similar cloud.
Demonstrated technical leadership: design docs, trade-off decisions, mentoring, incident ownership.
Product mindset: ability to balance model accuracy, latency, cost and user experience.
Prior work on large-scale scheduling or optimization problems (e.g. nurse-rostering).
Exposure to Kubernetes, Terraform or CDK.
Front-end empathy; willing to tweak a React component when needed.
Generous medical, dental, and vision plans.
Paid company holidays, sick time, and unlimited time off.
Monthly credits to spend on professional development, general wellness, Assembled customers, and commuting.
Paid parental leave.
Hybrid work model with catered lunches everyday (M-F), snacks, and beverages in our SF & NY offices.
401(k) plan enrollment.
