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Important information
Contract type:
Permanent contract
Salary / Daily rate:
90000
This job is at 0% commission 🎉Location:
Paris, France
Starting date:
Urgent
Work mode:
Hybrid
Published on:
25 March 2026
What they need
Job description
Verso is building the Consumer Intelligence OS.
We replace traditional qualitative research agencies with an AI-native platform that conducts, analyzes, and compounds consumer conversations at scale. Instead of one-off studies, companies get a continuous, structured understanding of their customers.
Our ambition is to make consumer insight a real-time, always-on system embedded in every product and marketing decision.
We’re starting with AI-moderated interviews — but this is just the entry point. The long-term product is a system of record for consumer understanding, powered by agentic workflows.
Backed by Hexa (eFounders), behind Aircall, Front, Spendesk, Yousign and 50+ others.
Verso is founded by an experienced team with a strong track record in strategy, technology, and company building.
Lydia - CEO: HEC graduate, 7 years at BCG (Principal), expert in strategy, innovation, and consumer goods
Camille - CTO: Dual degree X/HEC, former lead at CO2 AI, expert in AI and scalable product development
The role
You will join Verso as a Founding Engineer to own systems end-to-end → from infrastructure to product, with no strict boundaries — and help define engineering standards, observability, and the foundations of the team.
You will design and scale a coherent AI system end-to-end, at the intersection of agents, data, and product:
An AI interviewer interacting with users at scale (voice & video), sustaining long-form conversations with high reliability
Agent systems (state, memory, evaluation loops) orchestrating complex interactions
Real-time and async pipelines transforming multimodal data (video, audio, text) into structured, actionable insights
A client platform to explore these insights through an AI-native interface
Data systems to structure, query, and reuse qualitative knowledge at scale
Optimization of latency, cost, and performance across LLM, TTS, STT
Reliability of long-running AI workflows in production
What makes this role unique
You will build production-grade AI systems, not prototypes
You will work on long-form, high-stakes interactions, not short prompts
You will design systems where AI quality is as critical as system reliability
You will operate at the intersection of AI, data, infrastructure, and product
Current stack
Frontend: TypeScript, React, Tailwind, shadcn
Backend: Python, FastAPI, SQLAlchemy
Infra: Vercel, Railway, Supabase → AWS
AI: LLM orchestration, STT/TTS pipelines, eval systems (rapidly evolving)
Profile wanted
Profile
We’re flexible on background, but we’re looking for people with a strong builder mindset.
Some seniority (not straight out of school), but not necessarily 10+ years — 2–3 years in a strong environment is enough
Experience in early-stage or high-ownership environments
Strong bias toward action, autonomy, and ownership
We heavily overweight mindset over credentials:
Product and user-first thinking
Pragmatism and speed
Optimism and resilience
High intellectual honesty
Strong drive
On hard skills, although Python and Typescript is a good +, but we don’t optimize for specific languages. What matters is:
Ability to design and scale production systems
Comfort with ambiguity and messy real-world problems
Ideally some exposure to AI / agentic systems in production (but not required)
TL;DR: we’re looking for people who are sharp, driven, and with whom we could realistically build a company.
Preferred Experience
Building production-grade AI systems on messy, real-world data (not demos)
Owning systems end-to-end (architecture → production → iteration)
Experience with complex systems (real-time, pipelines, or LLM-based products)
Strong intuition for trade-offs: latency, cost, quality, reliability
Motivation to build a category-defining company with long-term ownership
Why Join
Category shift: we’re turning consumer understanding into a continuous system — not just improving research workflows
Hard problems: production AI over multimodal, unstructured data (audio, video, text, structured), across:
a B2C product (low latency, high scale)
a B2B platform (enterprise-grade reliability, security, integrity)
Full ownership: architecture, product, and execution from day one
Meaningful equity: early-stage package with strong upside (next fundraise soon)
Hexa environment: access to top-tier talent, resources, and a strong builder ecosystem
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