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Alljoined

Machine Learning Researcher

San Francisco$140k–$250kfulltimemidAdded 1 month ago

About this role

Alljoined is seeking a Machine Learning Researcher to develop advanced deep learning models for non-invasive brain-computer interfaces that decode neural signals from EEG data. You'll design state-of-the-art architectures, publish research at top conferences, and translate innovations into production systems alongside neuroscientists and engineers.

What you'll do

  • Design and train cutting-edge deep learning models for EEG-based neural decoding using modern architectures
  • Develop novel approaches for processing high-frequency time-series EEG and multimodal datasets
  • Convert research prototypes into production-quality code integrated with the BCI platform
  • Collaborate with neuroscientists and ML engineers on end-to-end neural decoding solutions
  • Publish findings at top-tier ML conferences and contribute to open-source projects
  • Work on multimodal representation learning, generative modeling, and temporal sequence modeling

What they're looking for

  • Deep learning model development and training
  • Multimodal representation learning (contrastive learning, masked autoencoding)
  • Generative modeling (diffusion models, transformers, GANs)
  • Temporal sequence modeling (state-space models, transformers)
  • Python and PyTorch proficiency
  • Distributed training and ML infrastructure
  • High-quality research publication track record
  • Production-grade coding and code review standards

Benefits

  • Housing support options
  • Visa sponsorship
  • Health insurance
  • 3% 401k matching
  • Equity compensation
  • Competitive salary range $140,000–$250,000/year
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Alljoined

Alljoined builds infrastructure and machine learning systems for non-invasive brain-computer interface research, focusing on processing and decoding EEG neural signals at scale. The company is hiring Software Engineers, Machine Learning Researchers, and Data Infrastructure Engineers to develop data pipelines, deep learning models, and backend systems that power neural decoding experiments.

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Likely interview questions

  • Walk us through a recent project where you developed a deep learning model for temporal sequence data. What architecture did you choose and why?
  • Describe your experience with multimodal representation learning. Have you implemented contrastive objectives like CLIP or masked autoencoding approaches?
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