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Founding AI Engineer

United States, New York Employment contract 200000.00 - 250000.00 USD / Year · Job Posted May 27, 2026
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Job Description

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

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