Chief Science Officer at Sereact GmbH

Chief Science Officer @ Sereact 🦾

Chief Science Officer (m/f/d) · Zurich or Stuttgart, Boston possible · Reports to the CEO · Full-time

Who we are

Sereact builds the robot brain for physical work. Cortex, our vision-language-action foundation model, is hardware-agnostic: the same model runs single-arm and dual-arm systems, mobile manipulators and humanoids that pick, sort and handle returns in live production across Europe and the US. Customers include Zalando, IKEA, BMW, Rohlik and DHL.

Founded in 2021 in Stuttgart, with an R&D hub in Zurich and a US office in Boston. We have raised more than $145 million, most recently a $116 million Series B led by Headline, with Zalando as a strategic investor. We work in small, senior teams with a flat structure; execution counts more than titles.

Why this role, why now

Our robots produce data no lab can stage. Our fleet has recorded more than 500 million manipulation interactions in live warehouse operations; Cortex 2.0 was trained on a curated 10 million of them and added a world model that plans before it acts ( arXiv 2604.20246 ). Cortex 2.5 learns a new task from a few demonstrations instead of a training run. Deployments are growing from single robots to fleets, and the research team is growing with them.

We now want one scientific leader who owns the research behind Cortex end to end and turns what our fleet experiences every day into the next step change in what robots can do.

The Real-World Learning Loop is your system

Sereact is a frontier research lab and a production-grade robotics company in one. Our bet: general-purpose robotic intelligence “must be learned from live deployments” ( The Real-World Learning Loop , Feb 2026). Every stage of that loop is yours to raise:

Every deployment teaches the next one. Your job is to make that loop turn faster and teach more each time.

Responsibilities

Not in scope, by design

What makes this role hard

Your first 12 months

What you bring

Nice to have: large-scale pretraining of foundation models · fleet learning or data engines · industrial manipulation · experience in both academia and industry.

Three questions we will ask you

  1. Tell us about a model you took from a paper to a robot that ran for weeks without you. What broke first?

  2. If you had our deployment data tomorrow, which bet would you make first, and what would you stop?

  3. How do you decide what to publish and what to keep?

What we offer

How we hire

Conversations with both founders, a research deep dive with the team, and a working session on a real problem from our deployments. We move fast.

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