ZipRecruiter

Log In

1

Machine Learning Neuroscience Postdoc Jobs (NOW HIRING)

Alljoined

Machine Learning Researcher

San Francisco, CA · On-site

$140K - $250K/yr

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 ...

nudge

Machine Learning Engineer

San Francisco, CA · On-site

About the role As a Machine Learning Lead at Nudge, you will drive the development of next-generation ML and imaging systems at the intersection of ultrasound, signal processing, and neuroscience.

previous pagenext page

Showing results 21-40

All JobsMachine Learning Neuroscience Postdoc Jobs

Machine Learning Neuroscience Postdoc information

What is a machine learning neuroscience postdoc?

A Machine Learning Neuroscience Postdoc is a postdoctoral researcher who applies machine learning techniques to analyze and interpret neuroscientific data. This role often involves developing and implementing computational models to understand brain function, neural networks, or behavior. Postdocs in this field typically collaborate with experimental neuroscientists and computer scientists to advance research in areas such as brain imaging, neural decoding, and cognitive function. Their work contributes to both neuroscience discoveries and innovations in artificial intelligence.

What are the key skills and qualifications needed to thrive as a machine learning neuroscience postdoc, and why are they important?

To thrive as a Machine Learning Neuroscience Postdoc, you need a strong background in neuroscience, computational modeling, and machine learning, typically supported by a PhD in a relevant field. Proficiency with programming languages such as Python or MATLAB, experience with neural data analysis tools (e.g., PyTorch, TensorFlow), and familiarity with statistical software are essential. Critical thinking, collaboration, and strong scientific communication skills help distinguish candidates in this interdisciplinary field. These skills and qualities are crucial for advancing research, solving complex problems, and effectively sharing findings with both scientific and broader audiences.

What are some common challenges faced by a machine learning neuroscience postdoc, and how can they be addressed?

A Machine Learning Neuroscience Postdoc often encounters challenges such as integrating complex neural data with advanced computational models, staying updated with rapidly evolving machine learning techniques, and effectively collaborating with interdisciplinary teams. Balancing deep dives into neuroscience literature while developing and validating new algorithms can be demanding. To address these challenges, it's helpful to participate in regular lab meetings, seek mentorship from both computational and experimental experts, and allocate time for continuous learning through workshops or conferences. Building a strong professional network also supports collaboration and skill development.

What are popular job titles related to Machine Learning Neuroscience Postdoc jobs?

For Machine Learning Neuroscience Postdoc jobs, the most frequently searched job titles are:

Infographic showing various Machine Learning Neuroscience Postdoc job openings in the United States as of August 2026, with employment types broken down into 1% As Needed, 73% Full Time, 23% Part Time, 1% Temporary, and 2% Contract. Highlights an 88% Physical, 2% Hybrid, and 10% Remote job distribution.

Machine Learning Researcher

San Francisco, CA • On-site

Alljoined
See more jobs at Alljoinedexternal

$140K - $250K/yr

Full-time

Medical

Re-posted 6 days ago

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

Apply