---
title: 'Genesis-World: Core Physics Engineer at Genesis'
canonical: 'https://feeny.ai/job/genesis-world-core-physics-engineer-genesis-london-af6gdh0cnbhn'
type: 'job'
last_seen: '2026-09-10'
---

# Genesis-World: Core Physics Engineer at Genesis

- **Company:** Genesis
- **Location:** London, United Kingdom
- **Employment:** full-time
- **Work type:** hybrid
- **Posted:** 2026-09-07
- **Last confirmed live:** 2026-09-10
- **Apply:** https://jobs.ashbyhq.com/genesis/690e8a27-8a56-42ad-8bd7-acd845673d05

## Job description

## What we're building

Robots will learn in simulation before they hit the factory. Genesis-World is our bet on that future. [Genesis-World](https://github.com/Genesis-Embodied-AI/genesis-world) is an open-source, general-purpose simulation platform for physical AI from [Genesis AI](https://www.genesis.ai/). One unified multi-physics engine: rigid bodies, FEM, MPM, particles, cloth, fluids. A robot arm can pour water onto sand, grasp a deformable object, or cut a soft body, all in the same simulation. Nyx, our in-house renderer, may be the most promising renderer for robotics out there: real-time photo-realistic rendering, advanced features like depth of field, and state-of-the-art techniques never seen before. Sensors of every kind: cameras, lidar, IMU, contact forces, temperature, plus arguably the most advanced tactile simulation available ([paper](https://openreview.net/forum?id=md8q2kfZHP)). And the engine keeps growing: we are developing internally the most comprehensive and fastest Incremental Potential Contact ([paper](https://ipc-sim.github.io)) solver for deformable body dynamics we know of, soon to be open-sourced. It powers real business applications, from full-fledged box packaging with labelling machine and all, to wire harnessing and lab automation, without any physics hack or compromise. Everything is Python-first and runs anywhere. Kernels are written once, and Quadrants, our in-house JIT compiler, lowers them to CUDA, AMD ROCm, Apple Metal, Vulkan, x86, and ARM64. A single laptop or a datacenter. Massively batched GPU simulation for learning at scale, and complex non-batched scenes where CPU wins outright. This is at [the core of Genesis AI's strategy](https://www.genesis.ai/blog/the-role-of-simulation-in-scalable-robotics-genesis-world-10-and-the-path-forward). Evaluation is the bottleneck of scalable robotics: real hardware caps iteration at wall-clock time, but simulation turns it into a compute problem. Ours already runs two orders of magnitude faster than hardware (tens of thousands of episodes in half an hour instead of 200+ hours), while correlating with on-hardware rollouts at 89%. The north star: physical AI that improves at the speed of compute.

## The role

You push the physics of Genesis-World forward. The mandate is clear: ship production-ready simulation capabilities that matter for the company's internal needs. Research applied end-to-end, from algorithm to merged, tested, documented code that real robot-learning pipelines depend on. Occasional groundbreaking research happens, notably through academic collaborations. But the core of the job is making the engine measurably better along five axes:

- Speed. Algorithms that are not only faster but also smart enough to spend compute only where it matters across both time and space: larger stable timesteps, selective fidelity (adaptive across scales or simply hand-set), structure-aware solvers.
- Completeness. No physics off limits: water, human animation, air flow, gravel, tendons, even body organs. Whatever the next use-case needs, the engine grows to cover it.
- Fidelity. More realistic models: contact, friction, deformation, energy, actuation, materials…
- Versatility. Extensible multi-physics without compromise on realism: all solvers in the scene coupled together at once, two-way and constraint-based. Write your own solver and it joins the scene like a native one, growing into an open solver ecosystem.
- Scalability. From workstation to factory scale, and one day, city scale: thousands of interacting entities, batched across environments, without losing physical soundness.

Our ambition is to establish Genesis-World as the go-to simulator for physical AI, from companies and research labs to individuals.

The problems waiting for you

- Every fidelity for every physics. The same physics at every point of the speed-accuracy spectrum, from heavily batched training with XPBD or VBD to final validation with IPC. Same scene, same API, pick your tradeoff.
- Invent physics level-of-detail (LOD). Rendering has had LOD for decades, physics is still waiting. Simulate at full fidelity what agents interact with and see, coarsely what they do not.
- Heterogeneous environments. Every parallel world can hold a completely different model: different bodies, joints, and collision geometries.
- Adaptive timesteps per island. Error-based control with Runge-Kutta Dopri5, and Time-of-Impact stepping during collision detection, as done in Jiminy.
- Couple everything, exactly. Efficient and accurate two-way constraint-based coupling between heterogeneous grey-box solvers, using state-of-the-art methods like ADMM. Owning every solver in the stack is what makes it possible.
- More scalable constraint solvers. Push rigid constraint solving beyond its current scalability ceiling ([reference](https://www.roboticsproceedings.org/rss20/p108.pdf)).
- Unify contact resolution. Hydro-elastic compliance, unilateral constraints, and sequential impulses in the same framework, ideally under one generic formulation.
- Closed kinematic loops without constraints. Handle loops intrinsically for numerical stability and speed, in the spirit of [Kamino](https://disneyresearch.github.io/kamino).

Day to day: you write your physics in plain Python and Quadrants makes it fast on every backend. And you validate it the hard way: analytical closed forms, other engines, real-world data.

## Who you are

You are a physicist and an engineer at once. You judge a method by whether it holds up in production at real scale, and you do not stop until it does. No blind spots: you relentlessly hunt down even the defect that looks insignificant, because it never is.

- A strong background in physics-based simulation, preferably related to robotics: RBD, FEM, MPM, SPH, IPC, XPBD, VBD, ABD, plus constrained optimization and numerical integration of stiff systems.
- A track record of shipping simulation code that others rely on, in an engine, in industry, or in a research codebase used beyond its authors.
- Solid HPC programming (CPU and/or GPU), and an instinct for what makes a numerical method fast in practice, beyond complexity classes.
- Rigor in validation: analytical closed forms, cross-engine consistency, real-world data.

Bonus points: publications in simulation, graphics, or robotics venues (SIGGRAPH, ICRA, IROS, CoRL, RSS). Contributions to an open-source physics engine.

## About Genesis

## Company Overview
- **One-liner**: Genesis AI is a full-stack robotics company building general-purpose robots with human-level dexterity and intelligence, powered by its own foundation model and hardware.
- **Entity Type**: Private (Seed stage – $105M raised)
- **Headquarters**: Paris, France (with offices in the San Francisco Bay Area and London)
- **Founded**: 2024 (emerged from stealth in July 2025)
- **Founders**: Zhou Xian (CEO), Théophile Gervet (President & Co-founder), Yi-Ling Qiao (Co-founder), Tsun-Hsuan Wang (Co-founder)

## Core Business
- **Primary industry**: Robotics Engineering / General-Purpose Robotics
- **Target customers**: Enterprises in manufacturing, laboratories, hospitals, and logistics; future expansion into homes.
- **Mission**: “Build the world’s most capable robots that unlock new possibilities for humans.”

## Products & Services
- **Eno (General-Purpose Robot)**: A full-body robot designed to operate reliably in factories, labs, hospitals, and homes. Driven by the GENE foundation model, it features 20 active, back-drivable degrees of freedom in its hands – matching human size and dexterity.
- **GENE-26.5 (Foundation Model)**: The AI model that powers Eno. It understands goals, reasons through changing conditions, and completes tasks end-to-end. Trained on massive human-based internet video data and proprietary simulation.
- **Data Collection Glove**: A lightweight, sensor-loaded glove worn by humans to collect high-quality manipulation data in real-world settings (e.g., lab technicians, manufacturing workers). This glove bridges the “embodiment gap” and feeds the model with human skill data.
- **Simulation System**: A proprietary simulation environment that accelerates model training and evaluation, reducing the iteration bottleneck.

## Market Standing
- **Valuation**: Not disclosed
- **Key Metric**: Total funding of **$105M** (Seed round, July 2025)
- **Notable Investors/Partners**: Eclipse (co-lead), Khosla Ventures (co-lead), Bpifrance, HSG; individual investors Eric Schmidt, Xavier Niel, Daniela Rus, Vladlen Koltun. Hardware manufacturing partner: Wuji Tech (China).
- **Growth Signals**:
  - Headcount grew from stealth to ~53–60 employees across Paris, Bay Area, and London (monthly growth +16% on LinkedIn).
  - 29 active job postings as of mid-2026, with a +262.5% monthly increase in openings.
  - Demonstrated a wide range of manipulation tasks (cooking, piano, Rubik’s cube, lab work) in public demos.
  - In talks with multiple customers for commercial deployment in pharma and manufacturing.

## Competitive Advantages
- **Full-stack integration**: Genesis controls both the AI model and the robotic hardware, enabling tighter optimization and faster iteration than companies that only do software or hardware.
- **Human-like dexterity**: The robotic hand matches human size, shape, and 20 degrees of freedom, allowing direct transfer of human demonstration data without an “embodiment gap.”
- **Data flywheel**: The combination of a low-friction data collection glove, simulation, and egocentric video creates a scalable pipeline for building a “human skill library.”
- **World-class team**: Founders and early engineers have backgrounds from Mistral AI, NVIDIA, Google DeepMind, Apple Intelligence, Unity, Epic, and leading robotics labs (Berkeley, CMU).

## Strategic Focus
- **Go full stack**: Continue integrating model, hardware, and data collection to deliver a complete general-purpose robot.
- **Commercial rollout**: Target early customers in pharmaceutical labs, manufacturing, and other industrial settings where dexterous manipulation is critical.
- **Scale data collection**: Expand the use of the data glove (both internally and with third-party partners) to rapidly grow the model’s skill repertoire.
- **Global hiring**: Aggressively grow the team across Paris, Bay Area, and London, especially in ML, simulation, rendering, and robotics engineering.

## Why Work Here
- **Culture**: Values include candor, responsibility, limitless ambition, and a joyful journey. The team is described as multicultural, optimistic, and pragmatic.
- **Work model**: Hybrid/office-based presence in Paris, San Francisco Bay Area, and London. No explicit remote policy mentioned, but the company emphasizes in-person collaboration.
- **Engineering culture**: Builders get to work on the hardest problems in robotics and AI – from foundation models to hardware design to data systems. The team includes pioneers of generative simulation, Diffusion Policy, and GPU compilers.
- **Notable perks**: The chance to shape a category-defining general-purpose robot; close collaboration with world-class investors and advisors; fast-growing startup with significant resources ($105M seed).

## Sources
1. [Genesis AI – Official Website](https://www.genesis.ai/)
2. [Genesis AI – Careers Page](https://www.genesis.ai/careers)
3. [TechCrunch – “Khosla-backed robotics startup Genesis AI has gone full stack, demo shows”](https://techcrunch.com/2026/05/06/khosla-backed-robotics-startup-genesis-ai-has-gone-full-stack-demo-shows/)
4. [LinkedIn – Genesis AI Company Profile](https://www.linkedin.com/company/gs-ai)
5. [robotics.press – Genesis AI Company Profile](https://robotics.press/news/genesis-ai-company-profile/)

## Other roles at Genesis

- [Training / AI Infrastructure](https://feeny.ai/job/training-ai-infrastructure-genesis-london-3dr9j4jj5bpg) — London, United Kingdom
- [Quadrants: Compiler Lead](https://feeny.ai/job/quadrants-compiler-lead-genesis-london-12t3masvvhtb) — London, United Kingdom
- [Simulation Infrastructure](https://feeny.ai/job/simulation-infrastructure-genesis-london-kvht3jcazypw) — London, United Kingdom
- [Genesis-World: Core Simulation Engine Engineer](https://feeny.ai/job/genesis-world-core-simulation-engine-engineer-genesis-london-nahhy0a9v4xc) — London, United Kingdom
- [Robot Teleoperation Specialist](https://feeny.ai/job/robot-teleoperation-specialist-genesis-london-mcm3zmdg9rkz) — London, United Kingdom
- [Robot Learning](https://feeny.ai/job/robot-learning-genesis-london-jm6tc1tv6t6m) — London, United Kingdom
- [Site Reliability Engineer](https://feeny.ai/job/site-reliability-engineer-genesis-london-cpq05vt4mpzk) — London, United Kingdom
- [QA Engineer: Nyx Renderer & Genesis World](https://feeny.ai/job/qa-engineer-nyx-renderer-genesis-world-genesis-london-2zks5hza6tsh) — London, United Kingdom
- [Technical Program Manager](https://feeny.ai/job/technical-program-manager-genesis-london-2mt52dmhg247) — London, United Kingdom
- [Data Agent](https://feeny.ai/job/data-agent-genesis-london-pab7w4z0v01e) — London, United Kingdom