---
title: 'Genesis-World: Core Simulation Engine Engineer at Genesis'
canonical: 'https://feeny.ai/job/genesis-world-core-simulation-engine-engineer-genesis-london-nahhy0a9v4xc'
type: 'job'
last_seen: '2026-09-10'
---

# Genesis-World: Core Simulation Engine 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/70404b8d-95ec-4670-af21-abecee6dfe74

## 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

Simulation is still a hard sell in robotics. Outside a few success stories, like reinforcement learning for locomotion, most teams skip it, and friction is a big part of why: painful to use, painful to debug. We live that pain ourselves: debugging a failing experiment during policy evaluation takes 30 minutes, and there is no way to automatically generate a standalone reproduction script. That blocks adoption, even internally. Your job is to kill that friction. You build the software engineering backbone of Genesis-World: everything that makes a large, fast-moving simulation codebase reliable, maintainable, frictionless. And it all ships in the open. Every hour of pain you remove, you remove for every roboticist who comes after you. You join as core maintainer of Genesis-World, with shared stewardship of the whole platform from day one. Our physics team develops the algorithms, but just as importantly, you work to make everything around them excellent: infrastructure, tooling, APIs, architecture.

The problems waiting for you

- Make testing of a JIT-compiled engine scalable. Running our full suite is already taking hours because the ratio between compile time and runtime can be as bad as x10. Getting rid of this bottleneck is a serious challenge that will require a joint effort from the Genesis-World and the compiler teams.
- Make any bug reproducible. Dump and reload simulations bit-exactly, across machines, across backends, even when a single timestep of a batched simulation can already max out VRAM. Turn any failing run into a standalone reproduction script, automatically.
- Make autodiff first-class. Genesis-World is differentiable today, but partially: memory-hungry, slower than it could be, and hard to maintain due to manual operation recording. The project spans the engine and the compiler: you drive the requirements on Quadrants and iterate in a tight loop with the compiler team until the integration is solid.
- Edit scenes at frozen time. See the scene, move entities and robots around, iterate on layout, all without building the scene or compiling the engine. Authoring today means a full build cycle. Make it instant.
- Make the simulator extensible. First-class plugins for physics solvers and interactive viewer backends, including Nyx. Sensors already work this way. Generalize it, and Genesis-World becomes a platform others build on.
- Give robots real actuation. Motors and MIMO transmissions, cleanly abstracted at the interface between users and the physics engine.
- Make time a local variable. Today the clock and the timestep are global to the engine, which forbids whole classes of speedups: environments cannot step independently, the timestep cannot adapt to the state, and individual solvers cannot advance at their own rate. Free the clock down to the sub-scene level and let the simulation fly.
- Make scenes come alive. Events triggered by physics: press the button and the microwave door pops open, squeeze the trigger and the drill spins.
- Keep the core sharp. Refactoring legacy subsystems, stronger typing, consistent coding style, less Python overhead on hot paths, full scene serialization, telemetry and replay.

Beyond the code: you review issues and pull requests with the core team, answer questions from users and contributors, and can join the push on adoption.

## Who you are

You care about the engineer using what you build as much as the thing itself. A slow test, a cryptic error, a manual step: you see it, you go after the root cause, and you remove it for everyone. You have opinions about API design. You are comfortable with physics and robotics: the right design choices take a working picture of what a simulator like Genesis-World does, how it works, and how it is used, a picture you can partly build on the job.

- Strong Python engineering on large codebases: architecture, refactoring, typing, packaging, performance profiling (py-spy, Nsight, Xcode Instruments, RenderDoc).
- Developer tooling, test infrastructure, or CI at scale.
- Enough fluency with JIT compilation, code generation, or GPU computing stacks to reason about compilation caching, kernel dispatch, and startup cost. You will work closely with Quadrants.
- At ease debugging across the full platform matrix: Windows, Linux, and macOS, on x86 and arm64, over CUDA, AMD, and Apple Metal (and possibly Intel XPU via Vulkan soon)
- Open-source reflexes: triaging issues, reviewing external pull requests, communicating with a community.

Bonus points: experience with other robotics simulators (Drake, MuJoCo, Newton, Isaac, RaiSim, Jiminy, Brax). PyTorch, our user-facing interface. Other JIT compilers such as Triton, JAX, or Numba. 3D tooling and interactive editors (Maya, Blender, Houdini). Serialization formats (USD, glTF, MJCF/URDF). C++, CUDA, or Metal. Developer relations or community work.

## 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 Physics Engineer](https://feeny.ai/job/genesis-world-core-physics-engineer-genesis-london-af6gdh0cnbhn) — 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