Assembled Products Corporation

• Build product experiences end to end — Own customer-facing features from concept through production across frontend, backend, APIs, data models, and AI-powered workflows. • Lead frontend implementation for complex products — Build intuitive, performant user experiences while bringing strong judgment around frontend architecture, state management, and application design. • Strengthen our frontend foundations — Improve performance, maintainability, developer experience, and technical quality across our React application. Drive major upgrades and architectural improvements when needed. • Create reusable systems and patterns — Build thoughtful abstractions, components, and frameworks that help the team move faster without sacrificing product quality. • Partner deeply with product and design — Turn ambiguous customer problems into elegant product experiences and iterate quickly based on customer feedback. • Shape the future of AI-assisted software development — Evaluate, experiment with, and operationalize AI coding agents and development workflows. Help us determine how engineers and AI work together to build products faster, with higher quality, and greater leverage.

• Own the technical direction of our design system, from token architecture and components to tooling, testing, and adoption • Shape how AI coding agents build UI, through component APIs, documentation, guardrails, and developer workflows • Modernize our frontend foundations, including component architecture, tooling, and development patterns • Raise the bar for frontend quality, including performance, accessibility, responsiveness, and interaction detail • Build the infrastructure that keeps the system reliable at scale, including testing, visual regression, CI/CD, and versioned releases • Establish frontend best practices across teams, helping engineers build more consistent, maintainable, and high-quality user experiences • Influence product decisions early, surfacing technical constraints, tradeoffs, and opportunities throughout the design process

• Open an Engineering Manager role on the Forecasting and Scheduling team • Build algorithms and user experiences to optimize staffing • Set technical direction and make product decisions • Partner closely with PM, design, and ML engineers • Work directly with customers to ensure product efficacy

• Developing forecasting interfaces, data pipelines, and inference servers to predict support contact volume and determine the optimal number of support agents. • Designing and implementing interfaces to collect and store team preferences and customer business constraints. • Enhancing machine learning efficiency and operations to support rapid model deployment and iteration.

• 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. • MLOps: Enhancing machine learning efficiency and operations to support rapid model deployment and iteration.

• Process customer renewals end-to-end in Salesforce, following established workflows (this is a high-volume, detail-intensive core responsibility) • Support Tier 1 ticket handling in Zendesk — triaging, routing, and resolving common issues per playbook • Maintain data accuracy across tools and flag discrepancies or anomalies to the team lead • Contribute to documentation and process improvement as workflows evolve • Take on additional operational tasks as the needs of the business shift • Handle back office CS operations tasks including account updates, data hygiene, and queue management in Salesforce • Audit and clean account data in Salesforce to ensure accuracy of the source information powering AI-generated account summaries for CSMs

• Identify high-potential businesses and verticals and develop and execute outbound strategies to bring them to Assembled • Demonstrate an ability to multithread and access C-level executives • Clearly articulate and demonstrate our value proposition, creating excitement and enthusiasm among prospects. • Run effective sales processes from start to finish — including demos, negotiation, security and procurement • Be a trusted advisor to prospective customers • Work cross-functionally with Customer Success, Marketing and Engineering to ensure customers are onboarded and set up for success • Use your learnings to build and iterate on our sales philosophy, playbook and processes

• Build foundational new features such as implementing translation capabilities powered by LLMs • Improve LLM model results using techniques like vector search • Develop LLM Infrastructure and design evaluation and logging systems • Engage with customers to understand their needs and improve product • Collaborate across various roles including coding and user research • Shape the team culture by encouraging initiative and product quality

• Building high-quality software for our voice AI platform, from rapid prototypes that push the boundaries of what's possible to production-ready, scalable solutions • Continuously improving our AI capabilities and accuracy through experimentation, data analysis, and innovative approaches • Implementing and optimizing LLM and voice technology while balancing intelligence, latency, and cost • Collaborating across engineering and cross-functional teams to tackle challenging technical problems throughout the full lifecycle of our voice AI products - from ideation and prototyping to deployment and monitoring • Develop voice-specific product features from the ground up, such as implementing voice recognition capabilities powered by LLMs and intelligent categorization of incoming calls. You'll help design and build intuitive interfaces for support agents to monitor and interact with AI voice assistants. • Enhance our voice recognition and generation engine using advanced techniques. You'll help us leverage implicit knowledge bases to improve model performance in voice contexts. • Architect the abstractions that enable integration of various types of LLMs tailored for voice applications. You'll design and implement evaluation and logging systems to monitor performance. • Collaborate with our customers (both support agents and managers) to understand how they interact with our voice product, and how we can improve their experience. • Be versatile in roles — coding, user research, planning, brainstorming, and cross-team collaboration. • Encourage a startup mentality focused on product quality and taking initiative.

• Developing forecasting interfaces, data pipelines, and inference servers to predict support contact volume. • Designing and implementing interfaces to collect and store team preferences and customer business constraints. • Enhancing machine learning efficiency and operations to support rapid model deployment and iteration.