Machine Learning Researcher

Machine Learning Researcher

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.

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