Machine Learning Researcher
Alljoined · San Francisco
About this role
## 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, advancing the frontier of neural decoding to unlock meaningful breakthroughs in human wellness and capability. ## About the Role We’re looking for a **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’ll work closely with leading experts in neural decoding and AI to push the boundaries of what’s possible in **brain-computer interfaces (BCIs)**—balancing ambitious research with rigorous, production-quality engineering. ## What You’ll Work On - Develop, train, and refine state-of-the-art deep learning models for neural decoding, leveraging recent advances such as **transformers** and **diffusion models**. - Explore novel methods for modeling **high-frequency, time-series EEG** data alongside adjacent data modalities. - Translate research insights into **production-grade code** integrated 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 ML/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 (e.g., artificial intelligence, computational neuroscience, mathematics, biomedical engineering) **and 5–7 years** of experience in ML research or applied ML engineering; **or** - A graduate degree (M.S. or Ph.D.) in computer science or a related field **and at least 3 years** of experience in ML research or applied ML engineering. - A track record of high-quality research via publications in leading venues (e.g., **NeurIPS, ICML, ICLR, CVPR**) or respected journals. - Strong proficiency in **Python** and **PyTorch**, plus familiarity with modern ML tooling and **distributed training**. - Experience contributing to a **production-quality codebase** with modern code-review standards. - Candidates with a **Ph.D.** and/or experience in a high-profile ML research lab are strongly preferred. ## Areas of Relevant Expertise (Preferred) - **Multimodal representation learning:** CLIP-style contrastive objectives, masked autoencoding. - **Generative modeling:** diffusion models, transformer decoders, latent GANs. - **Temporal sequence modeling:** state-space models, STFT-aware transformers, RWKV. ## Benefits - Options for housing support - Visa sponsorship - Health insurance
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