--- title: 'Research Intern at Gensyn' canonical: 'https://feeny.ai/job/research-intern-gensyn-remote-kjte8rn6b8pd' type: 'job' last_seen: '2026-09-10' --- # Research Intern at Gensyn - **Company:** Gensyn - **Location:** Remote - **Work type:** remote - **Posted:** 2025-04-23 - **Last confirmed live:** 2026-09-10 - **Apply:** https://job-boards.eu.greenhouse.io/gensyn/jobs/4579609101 ## Job description Machine intelligence will soon take over humanity’s role in knowledge-keeping and creation. What started in the mid-1990s as the gradual off-loading of knowledge and decision making to search engines will be rapidly replaced by vast neural networks - with all knowledge compressed into their artificial neurons. Unlike organic life, machine intelligence, built within silicon, needs protocols to coordinate and grow. And, like nature, these protocols should be open, permissionless, and neutral. Starting with compute hardware, the Gensyn protocol networks together the core resources required for machine intelligence to flourish alongside human intelligence. ## The Role - Contribute to cutting-edge research in scalable, distributed machine learning systems alongside experienced researchers and engineers. Explore new ways of building and verifying neural networks that operate across huge, decentralised, topologies of heterogenous devices. ## Responsibilities - Contribute to original research in deep learning with a focus on modular architectures, verifiability, continual learning, and scale - Design and prototype novel neural network architectures for decentralized compute environments - Contribute to joint publications and projects in collaboration with academic and industry researchers targeting top-tier AI venues such as NeurIPS, ICML, and ICLR Competencies Must Have - Currently enrolled in a PhD program (or, in exceptional cases, in a Master’s program) in Computer Science, Machine Learning, or a related field - Prior experience conducting original research, ideally with authorship or co-authorship on ML papers - Strong understanding of deep learning fundamentals and experience working with in at least one major framework, e.g. PyTorch, JAX, or TensorFlow - Self-directed, curious, and able to thrive in an environment with high autonomy - Excellent written and verbal communication skills ## Preferred - Research experience in distributed systems, continual learning, or modular neural architectures - A desire to contribute to open research and collaborate with the broader ML research community ## Nice to Have - Experience at the intersection of cryptography and machine learning Please note: the benefits listed below apply to full-time employees only ## Compensation / Benefits - Competitive salary + share of equity and token pool - Fully remote work - we currently hire between the West Coast (PT) and Central Europe (CET) time zones - Visa sponsorship - available for those who would like to relocate to the US after being hired - 3-4x all expenses paid company retreats around the world, per year - Whatever equipment you need - Paid sick leave and flexible vacation - Company-sponsored health, vision, and dental insurance - including spouse/dependents [🇺🇸 only] Our Principles Autonomy & Independence - Don’t ask for permission - we have a [constraint culture](https://web.archive.org/web/20230526203438/https://www.youtube.com/watch?v=wLQ-rq4FgQA&t=2920s), not a permission culture. - Claim ownership of any work stream and set its goals/deadlines, rather than waiting to be assigned work or relying on job specs. - Push & pull context on your work rather than waiting for information from others and assuming people know what you’re doing. - Communicate to be understood rather than pushing out information and expecting others to work to understand it. - Stay a small team - misalignment and politics scale super-linearly with team size. Small protocol teams [rival](https://research.contrary.com/reports/uniswap-labs?head=;;business--;;model) much larger traditional teams. Rejection of mediocrity & high performance - Give direct feedback to everyone immediately - rather than avoiding [unpopularity](http://www.paulgraham.com/say.html), expecting things to improve naturally, or [trading short-term pain for extreme long-term pain](https://verraes.net/2013/08/antifragile-nassim-nicholas-taleb/). - Embrace an extreme learning rate - rather than assuming limits to your ability / knowledge. - Don’t quit - push to the final outcome, despite any barriers. - Be anti-fragile - balance short-term risk for long-term outcomes. - Reject waste - guard the company’s time, rather than wasting it in meetings without clear purpose/focus, or [bikeshedding](https://www.techtarget.com/whatis/definition/Parkinsons-law-of-triviality-bikeshedding). ## About Gensyn ## Company Overview - **One-liner**: Gensyn is building the decentralised infrastructure for machine intelligence, enabling peer-to-peer compute, data, and information exchange through open markets powered by cryptographic verification. - **Entity Type**: Private (Crypto/Protocol – token $AI) - **Headquarters**: London, UK - **Founded**: 2020 - **Founders**: Not publicly disclosed ## Core Business - Primary industry: Decentralised AI infrastructure, distributed machine learning, cryptography - Target customers: AI researchers, machine learning engineers, node operators, and web3 developers (B2B/B2C for developers) - Mission or purpose: To provide an open, trustless infrastructure layer where AI systems can train, verify, trade, and evolve without centralised control. ## Products & Services - **RL Swarm**: Open source framework for creating reinforcement learning training swarms over the internet. Enables distributed, collaborative RL across nodes. - **Delphi**: Fully on-chain information market where humans and AI agents trade predictions with automatic liquidity and trustless settlement. - **CodeAssist**: A private, local AI coding assistant that adapts to the user’s programming style and helps solve coding challenges. - **BlockAssist**: An AI assistant that learns from user actions in Minecraft, turning gameplay into a reinforcement learning environment. - **SkipPipe**: Open source pipelining framework for training LLMs in heterogeneous networks (research output). - **AXL**: Low-level networking library for peer-to-peer communication of ML weights, gradients, and signals. ## Market Standing - **Valuation/Market Cap**: Not publicly available (network token $AI trades on exchanges; no company valuation disclosed) - **Key Metric**: Total funding not publicly disclosed; mainnet is live and the network supports open information markets - **Notable Investors/Partners**: Not publicly listed - **Growth Signals**: 1,686 GitHub stars on RL Swarm; active open source community; live mainnet deployment; hiring for multiple senior roles (Head of Marketing, Technical Product Manager, Research Intern) ## Competitive Advantages - **Decentralised verification**: Cryptographic evidence that computations ran as specified, enabling trustless AI training and inference. - **Open infrastructure**: Permissionless participation for both humans and machines; no central gatekeeper. - **Information markets**: Novel mechanism for AI models to earn rewards based on prediction accuracy, creating a self-optimising loop. - **Full-stack approach**: From low-level networking (AXL) to end-user products (CodeAssist, Delphi) to research (SkipPipe, Verde). ## Strategic Focus - Expanding the Gensyn network’s compute and information market layers. - Driving adoption of open, decentralised AI through community contributions and research publications. - Building the “agentic bazaar” where AI models and humans trade information autonomously. ## Why Work Here - **Culture**: High autonomy, end-to-end ownership of meaningful problems; research and engineering overlap because the problems do. - **Remote/hybrid policy**: Fully remote (all listed roles are remote). - **Interview process**: Screening call → deep dive with research team → live coding test → final conversation with co-founders. - **Engineering culture**: Ship research and product in the open; work on decentralised ML, cryptography, and distributed systems; strong open source ethos. ## Sources 1. [gensyn.ai](https://www.gensyn.ai/) 2. [docs.gensyn.ai](https://docs.gensyn.ai/home.md) 3. [GitHub – gensyn-ai](https://github.com/gensyn-ai) 4. [Greenhouse careers page](http://job-boards.eu.greenhouse.io/gensyn/) 5. [gensyn.ai/careers](https://www.gensyn.ai/careers) ## Other roles at Gensyn - [Technical Product Manager](https://feeny.ai/job/technical-product-manager-gensyn-remote-ah1dsc2n08zq) - [Open Application](https://feeny.ai/job/open-application-gensyn-remote-3tv5nvmnwtsc) - [Research Intern](https://feeny.ai/job/research-intern-cantina-singapore-0t2dg9nfd7y1) — Singapore - [Research Intern](https://feeny.ai/job/research-intern-atla-london-jb06413zrkja) — London, United Kingdom - [Research Intern](https://feeny.ai/job/research-intern-haize-labs-new-york-0cbyc3dcxr68) — New York, NY - [Research Intern](https://feeny.ai/job/research-intern-ritual-remote-7cmnd1hagy5d)