# Robot Learning at Genesis AI

> Hybrid · France · Unspecified · Software engineering · open. Source: https://alderhq.app/jobs/ashby%3Agenesis%3Adfe0e827-a8ee-487f-b37e-763481fabd5a (read 2026-11-07).

## Facts (observed)

- Company: [Genesis AI](https://alderhq.app/companies/genesis-ai)
- Location text: Paris; London
- Where it can be done from (inferred): Hybrid · France (evidence: "Location: Paris; London")
- Posted: 2026-04-30 (191 days ago)
- First seen by alder: 2026-11-02 · last verified: 2026-11-04
- Reposted: 0 times · edited: 0 times
- Apply on the employer's site: https://jobs.ashbyhq.com/genesis/dfe0e827-a8ee-487f-b37e-763481fabd5a
- Skills listed (inferred): Python, C++

## Pay and sponsorship

- Posted pay: not posted
- Market band for similar posted roles (Paris, n 25): p25 €47k, median €62k, p75 €91k

## Description (the employer's text)

### What You’ll Do

Develop and optimize a learning-based robotic manipulation control stack

Design and maintain a teleoperation system with smooth, precise motion and low latency

Train robotic policies for manipulation and locomotion with reinforcement learning and imitation learning

Deploy robotic policies and diagnose latency or bottlenecks in the control pipeline

Analyze and minimize the sim-to-real gap by co-optimizing simulation and real-world robot behavior

Collaborate with a team of driven individuals committed to building general-purpose Physical AI

### What You’ll Bring

Passion for your craft and demonstrated excellence in robotics engineering

Exceptional ownership and initiative—finding and solving problems independently

Focus, attention to detail, patience, and a methodical approach to complex tasks

Production-level expertise in modern Python or C++

Extensive experience building and deploying real-world learning-based robotic systems (5+ years)

Bonus: Hands-on experience troubleshooting and maintaining robotic systems across mechanical, electrical, and software components

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Data: alder (https://alderhq.app). Cite https://alderhq.app/jobs/ashby%3Agenesis%3Adfe0e827-a8ee-487f-b37e-763481fabd5a and the date you read it. Observed facts come from the employer's own listing; location reach, level, family and skills are inferences.