Founding Engineer @Verso (backed by Hexa) - Permanent

Job description

Salary: 90000

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)

Desired profile

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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