--- title: 'Machine Learning Researcher at Alljoined' canonical: 'https://feeny.ai/job/machine-learning-researcher-alljoined-san-francisco-sn24fm8zxqk1' type: 'job' last_seen: '2026-09-07' --- # Machine Learning Researcher at Alljoined - **Company:** Alljoined - **Location:** San Francisco, CA - **Compensation:** $140k–$250k - **Employment:** full-time - **Work type:** onsite - **Posted:** 2025-11-05 - **Last confirmed live:** 2026-09-07 - **Apply:** https://jobs.ashbyhq.com/alljoined/537a054c-18ad-4e66-ab7a-178d1fe53ada ## Job description ## ABOUT ALLJOINED Alljoined is creating a future where humans are fully understood and augmented by technology. Our work solves the communication bottleneck between humans and computers by decoding thoughts from the brain, entirely non-invasively. We apply deep learning research to large scale neural datasets to decode internal thought directly. By advancing the frontier of neural decoding, we aim to unlock meaningful breakthroughs in human wellness and capability. ## ABOUT THE ROLE We are looking for a talented Machine Learning Researcher to join our core R&D team. You will design and implement advanced machine learning models for EEG-based neural decoding, contribute to high-impact research, and help build the foundational infrastructure behind our brain-decoding systems. You will work closely with leading experts in neural decoding and AI to push the boundaries of what is possible in brain-computer interfaces. This role sits at the intersection of ambitious research and rigorous engineering: you will explore novel modeling approaches while translating promising ideas into reliable, production-quality systems. ## WHAT YOU’LL WORK ON - Develop, train, and refine state-of-the-art deep learning models for neural decoding, drawing on recent advances in architectures such as transformers and diffusion models. - Explore novel methods for modeling high-frequency, time-series EEG data alongside several adjacent data modalities. - Translate research insights into production-grade code that integrates seamlessly with our in-house BCI stack. - Collaborate with neuroscientists and machine learning engineers to build scalable, end-to-end neural-decoding systems. - Publish findings at leading machine learning and AI conferences, including NeurIPS, ICML, ICLR, and CVPR. - Contribute to open-source communities where appropriate. YOU MAY BE A GOOD FIT IF YOU HAVE - A bachelor’s degree in computer science or a related field—such as artificial intelligence, computational neuroscience, mathematics, or biomedical engineering—and five to seven years of experience in machine learning research or applied machine learning engineering; or - A graduate degree (M.S. or Ph.D.) in computer science or a related field—such as artificial intelligence, computational neuroscience, or biomedical engineering—and at least three years of experience in machine learning research or applied machine learning engineering. - A track record of high-quality research, demonstrated through publications at leading machine learning conferences or in respected journals, including NeurIPS, ICML, ICLR, or CVPR. - Strong proficiency in Python and PyTorch, along with familiarity with modern machine learning tooling and distributed training. - Experience contributing to a production-quality codebase with modern code-review standards. Candidates with a Ph.D. and/or experience working in a high-profile machine learning research lab are strongly preferred. ## AREAS OF RELEVANT EXPERTISE We are particularly interested in candidates with experience in one or more of the following areas: - Multimodal representation learning: CLIP-style contrastive objectives and masked autoencoding. - Generative modeling: Diffusion models, transformer decoders, and latent GANs. - Temporal sequence modeling: State-space models, STFT-aware transformers, and RWKV. ## BENEFITS - Options for housing support - Visa sponsorship - Health insurance ## About Alljoined ## Company Overview - **One-liner**: Alljoined develops neural decoding technology to interpret thoughts from brain signals non-invasively, aiming to bridge human cognition and technology by applying deep learning to EEG and fMRI data. - **Entity Type**: Private (Seed round, January 2025) - **Headquarters**: San Francisco, California, United States - **Founded**: 2023 - **Founders**: Not publicly disclosed (CEO is Jonathan Xu) ## Core Business - **Primary industries**: Research Services, Artificial Intelligence, Brain-Computer Interfaces (BCI) - **Target customers**: B2B (research institutions, healthcare providers), B2C (future consumer BCI applications); currently research‑focused - **Mission statement**: “Create a future where people can better understand themselves and connect with technology” – developing neural decoding to augment human capability and dignity ## Products & Services - **ENIGMA Model**: A multi‑subject EEG‑to‑image decoding model that learns a shared visual language across brains, enabling adaptation to new users in minutes. Achieves state‑of‑the‑art accuracy (demonstrated in peer‑reviewed work). - **MindEye2**: Multi‑subject fMRI‑to‑image architecture that trains 40× faster than prior models while matching performance (ICML 2025). - **Public EEG Dataset**: Largest public dataset for EEG‑image decoding – 6+ hours of recordings per participant across 20 subjects using consumer‑grade hardware. - **Proprietary Research Models**: Decoding of semantic content, emotion, inner speech, and intentional planning from brain signals. All offerings are research‑stage technology, not yet commercialized (pre‑revenue). ## Market Standing - **Valuation / Total Funding**: Not disclosed (Seed round in Jan 2025, 2 investors) - **Key Metric**: Total funding amount not publicly available; headcount 12 employees (LinkedIn) with conflicting report of 9 (BuiltIn) - **Notable Investors & Advisors**: - Individual investors include **Jeff Dean** (Chief Scientist, Google), **Guillermo Rauch** (CEO, Vercel), **Anastasis Germanidis** (Co‑founder, RunwayML), **Oliver Cameron** (Co‑founder, Odyssey), **Vas Bailey**. - Scientific Advisory Board: **Dr. Anil Seth** (Professor, University of Sussex), **Dr. Rufin VanRullen** (CNRS), **Dr. Arnaud Delorme** (Chief Architect of EEGLAB), **Dr. Thomas Naselaris** (University of Minnesota), **Dr. Tanishq Mathew Abraham** (CEO, MedARC). - **Growth Signals**: - Headcount grew **+142.9% YoY** (LinkedIn); currently 7 open positions across research and engineering. - Published papers at **ICML 2025** (MindEye2) and **CVPR** (mental imagery reconstruction from fMRI, highlight paper). - Released the largest public EEG‑image decoding dataset and demonstrated log‑linear neural scaling laws. ## Competitive Advantages - **Non‑invasive decoding**: Uses consumer‑grade EEG, making BCI practical for widespread use. - **Multi‑subject learning**: Models (ENIGMA, MindEye2) generalize across individuals, reducing calibration time from hours to minutes. - **Cutting‑edge research**: Peer‑reviewed publications at top ML/vision conferences; team includes world‑class neuroscientists and ML researchers. - **Ethical positioning**: Focus on augmenting human capability and dignity, not replacement. ## Strategic Focus - Decoding increasingly complex cognitive processes (emotion, inner speech, planning, abstract reasoning). - Scaling neural decoding through larger datasets and more powerful models. - Building a practical, non‑invasive interface for mental health, self‑understanding, and human‑computer interaction. ## Why Work Here - **In‑office culture** in San Francisco, CA (all roles require on‑site presence). - **Early‑stage environment** with ability to shape foundational technology; currently 12 employees. - **Research‑heavy team** – roles include Machine Learning Researcher, Computational Neuroscientist, Data Infrastructure Engineer, Software Engineer. - **Mission‑driven** – “improving human capability and dignity” with a focus on ethical augmentative AI. - **World‑class advisors** from top institutions (Google, CNRS, Princeton, etc.) and a highly collaborative team (former employees from Tesla, Snowflake, Microsoft, etc.). ## Sources 1. [alljoined.com](https://www.alljoined.com/) 2. [alljoined.com/about](https://www.alljoined.com/about) 3. [linkedin.com/company/alljoined](https://www.linkedin.com/company/alljoined) 4. [builtin.com/company/alljoined-inc](https://builtin.com/company/alljoined-inc) 5. 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