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Machine Learning ResearcherAlljoined • San Francisco, California, United States
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Machine Learning Researcher

Alljoined
  • US
    San Francisco, California, United States
  • US
    San Francisco, California, United States
Jetzt Bewerben

Über

About Alljoined Alljoined aims to solve the communication bottleneck between humans and technology by decoding thoughts from the brain, entirely non‑invasively. We apply deep learning research to large scale EEG datasets collected on affordable hardware to decode images, text, and video initially, and eventually moving to internal thought. We are state‑of‑the‑art in capabilities and are fully vertically integrated. Our goal is to develop a general consumer interface to completely transform what we can do at home and work.
We are actively growing our world‑class team of researchers to build the next interface to improve individual lives as well as the well‑being of society as a whole.
About the Role We’re seeking a talented Machine Learning Researcher to join our core R&D team. This role involves designing and implementing advanced machine learning models for EEG‑based neural decoding, publishing high‑impact research, and developing the core infrastructure for our brain decoding systems. You will work closely with leading experts in neural decoding and AI, pushing the boundaries of what’s possible in brain computer interfaces.
Key Responsibilities
Research &Model Development:
Develop, train, and refine state‑of‑the‑art deep learning models for neural decoding, building on the latest advancements in ML architectures (e.g., transformers, diffusion models, etc).
Explore novel approaches for modeling high‑frequency timeseries EEG datasets along with a number of adjacent data modalities.
Translate research insights into production‑grade code that integrates seamlessly with our in‑house BCI stack.
Collaboration &Publication:
Collaborate with a team of neuroscientists and ML engineers to create scalable, end‑to‑end neural decoding solutions.
Publish findings at top‑tier ML and AI conferences (e.g., NeurIPS, ICML, ICLR, CVPR) and contribute to open‑source communities where appropriate.
Qualifications
Educational Background &Experience:
Bachelor’s degree in Computer Science or a related domain (e.g., AI, Computational Neuroscience, Mathematics, Biomedical Engineering, etc), with 5‑7 years of experience in ML research or applied ML engineering; OR
Graduate degree (M.S., Ph.D.) in Computer Science or a related domain (e.g., AI, Computational Neuroscience, Biomedical Engineering) with 3+ years of experience in ML research or applied ML engineering.
Candidates with a Ph.D. and/or experience in high‑profile ML research labs are strongly preferred.
Technical Expertise:
Multimodal Representation Learning (CLIP‑style contrastive objectives, masked autoencoding)
Generative Modeling (diffusion, transformer‑decoders, latent‑GANs)
Temporal Sequence Modeling (state‑space models, STFT‑aware transformers, RWKV)
A track record of high‑quality research demonstrated by publications in top ML conferences or journals (e.g., NeurIPS, ICML, ICLR, CVPR).
Strong proficiency in Python and PyTorch, familiarity with ML tooling, and distributed training.
Experience working in a production‑quality codebase with modern code review standards.
Compensation Range $140,000 - $250,000/year + equity
While this represents our expected range based on market data, final compensation will be determined based on your specific qualifications and may be outside this range.
Benefits
Options for housing support
Visa sponsorship
3% 401k matching
Health insurance
#J-18808-Ljbffr
  • San Francisco, California, United States

Sprachkenntnisse

  • English
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