What We Do 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. What you’d build: Architecture. Multi-
layer agent systems that autonomously discover structure in raw conversation
data, then orchestrate downstream agents to act on it. This is systems design
at a level that no other company in our space is operating at. 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. There is a wide gap between a demo of an
AI product and an AI product that actually works for real customers. We are
hiring engineers who can build on the right side of that gap. Why this role
exists We increasingly believe the data layer is the whole game in AI. Most
tools start with a point solution and try to inject intelligence into it. We
are doing the opposite: build the intelligence layer first (from data), and
let the point solutions (sales coaching, lead scoring, business insights, CRM
updates, agentic workflows, voice agents) fall out as different activations of
the same underlying intelligence. The market demand is here. Our biggest
growth bottleneck is engineering velocity. Customers pull us into new features
every week, stretch the product by using it for more things (and break our
system in the process!), and bring us into CTO and CIO conversations about
broader AI transformation. Even our non-technical users are building their own
tools on top of us with Claude Code. Product velocity is where our recent
capital is going. We are building a small, elite team that ships fast and sits
in the same room. You will work next to the CTO and CEO, talk to customers
directly, and ship in hours and days rather than weeks and months. About you
You are familiar with how modern AI products are built and shipped. You have
worked with LLMs, agents, tool use, and the standard moving parts, and you
have opinions about what works and what does not. You have shipped real
software to real users. You can describe in detail something you built, what
broke, and how you fixed it. You move across the stack and have product
instincts. You do not need a clean spec to start. If you bring deep experience
in any of the following, tell us. We are hiring across a few different shapes
of this role, and these are the areas where we want strong owners. 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. Stack TypeScript across the stack, React and Vite on the
frontend. Postgres with Prisma. AWS infrastructure. LLMs from Bedrock and
OpenAI. We build our own agent code rather than using a framework. Perks &
Benefits Health & wellness: 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