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Voiceops is the intelligence layer for consumer-facing businesses. We turn millions of customer conversations into a live model of how the business runs, and use it to power agents that take on the work across sales, product, marketing, and operations. $12M raised, seed stage company. Customers include Motorola, Kin Insurance, and Capella University. The Technical Problem: Voice data is messy, unstructured, and massive. A single client of ours can generate millions of calls per year across dozens of product lines, geographies, and agent populations. Extracting reliable structure from that data, structure accurate enough to run production workflows on, requires multi-level agent systems that discover structure in the data, validate extractions, identify statistical patterns, trigger downstream actions, and continually learn, all autonomously.
Job Responsibility
Architecture. Multi-layer agent systems that autonomously discover structure in raw conversation data, then orchestrate downstream agents to act on it
Frontier. AI is moving so fast. Our non-technical users are building their own tools with Claude Code! The architecture has to evolve at this pace. You will help us keep the system at the edge of what's possible, beyond what our clients can dream of: new inference strategies, new model capabilities, new ways to get 10x more out of the same team and system
Scale. We process millions of calls. The pipeline has to be fast, reliable, and cost-efficient. You'll work on the infrastructure that makes this work
Requirements
3+ years
Amazon Web Services (AWS)
PostgreSQL
React
TypeScript
LLMs
AI Agents
Nice to have
Building AI agents that operate over large or messy datasets, with thoughtful sampling, summarization, and bounded queries
Designing multi-agent systems and pipelines. Layered agents, generator and critic loops, planners and executors, and orchestration where one agent's output drives another's work
Agentic chat that reasons over real customer data and can act on it
Building AI products that ask very little of the user. This means the UI itself, plus the scaffolding behind it: smart defaults, agent harnesses, and prompt structures that let the agent do the heavy lifting on the user's behalf
Evals, observability, and the feedback loops that turn customer feedback into measurable product improvements
Voice agents of any kind. Outbound or inbound calling, real-time conversational systems, coaching, or anything else where audio is in the LLM loop
Workflow or pipeline builders where users compose actions through chat
What we offer
100% employer-paid insurance premiums for employees with options to add family members at low cost
Flexible PTO
Seed-stage equity grant
401(k) with employer match: 100% match on the first 3% of pay, 50% on the next 2%
Company-paid life insurance, short-term and long-term disability