Software Engineer - Platform

Assembled, Inc.
New York, United States
3 months ago
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Role details

Contract type
Permanent contract
Employment type
Full-time (> 32 hours)
Experience level
Expert
Experience required
5 years minimum
Working hours
Regular working hours
Job source

Tech stack

Artificial Intelligence Amazon Web Services Microsoft Online Services Cloud Computing Data Stores Python (Programming Language) PostgreSQL Redis Systems Integration Snowflake Backend Stripe
+3 more
Kubernetes Data Management Etsy

Job description

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., We’re looking for an experienced software engineer to help build the foundation of Assembled’s platform. Our Platform org is responsible for the core systems that power Assembled’s products end-to-end: the infrastructure our applications run on, the integrations that ingest data into the Assembled ecosystem, and the data platforms that transform, model, and serve that data across the company.

Requirements

  • Have 5+ years of engineering experience involving backend, platform, or infrastructure systems
  • Are excited by the idea of working on foundational problems that cut across infrastructure, data platforms, and integrations
  • Thrive in ambiguous environments and enjoy figuring out the right abstractions, system boundaries, and tradeoffs
  • Care deeply about reliability, maintainability, performance, and building systems that empower other engineers
  • Enjoy collaborating cross-functionally and understand how platform work can unlock product and business impact
  • Have familiarity with one or more parts of our stack:
  • Cloud provider: AWS
  • Container orchestration: Kubernetes + Karpenter
  • Datastores: Postgres + PgBouncer, Snowflake, Redis
  • Languages: Go and Python

About the company

Assembled’s products sit at the center of some of the most operationally complex support environments in the world. Our workforce management product helps support teams plan and operate at scale, while our AI agents help automate work across chat, email, and voice. As both products grow, we need systems that are reliable, scalable, secure, cost-efficient, and capable of supporting both real-time operational workflows and large-scale analytical workloads.

By joining the Platform org, you’ll work on foundational problems across infrastructure, data platforms, and integrations. Depending on your background and interests, you may go deeper in one area, but all of our platform engineers help build and evolve the systems that power both our workforce management product and our AI agents.

Some projects owned by the Platform org

  • Scalable application infrastructure: We’re continuing to evolve the systems that run our products reliably and efficiently at enterprise scale, from container orchestration and observability to security and cost efficiency.
  • Integrations platform: We’re building the platform and connectors that ingest data from the systems our customers rely on most, including CRMs, ticketing systems, knowledge bases, communication platforms, and AI model providers.
  • Modern data foundations: We’re building the systems that transform raw product and integration data into reliable, queryable, derived datasets that power metrics, reporting, analytics, and other product experiences.
  • Real-time and analytical systems: Many of our products depend on both low-latency operational paths and heavier analytical workflows. We’re investing in systems that can support both without duplicating logic or creating inconsistent definitions.

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