Machine Learning Engineer

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Netholabs is building a foundation model of the brain and behavior across species, trained on large-scale multimodal neural and behavioral data such as EEG, MEG, and fMRI. As a Senior Machine Learning Engineer, you will design and train the core generative model, own training at scale, and develop representations across neural signals and behavior. You will work in a small team in person in San Francisco, shaping the modeling direction in an early-stage, research-heavy environment.

• Design, train, and iterate on large generative recurrent or transformer-based models over multimodal neural and behavioral data
• Own training at scale: data loading, distributed multi-node training, hyperparameter optimization, and evaluation
• Develop representations that capture structure across species and modalities
• Define and run experiments and build evaluation to validate modeling choices
• Prototype new ideas and turn research findings into reproducible, production-quality model code
• Partner with data engineering and research teams on data readiness and modeling roadmap

• Trained large deep learning models end to end in production or research settings
• Hands-on experience training transformer or other large sequence models, including distributed training and scaling
• Proficiency in Python and PyTorch with clean, reproducible coding practices
• Comfort working with large, messy, multimodal or time-series data
• Ability to learn new domains quickly and implement ideas from academic literature
• Strong communication, collaboration, and pragmatism for an early-stage environment

• Interest in neuroscience, biosignals, or computational cognitive science
• Experience with representation learning and self-supervised or generative modeling such as VAEs, GANs, diffusion models, and contrastive learning
• Familiarity with signal processing, especially for EEG, and time-series analysis
• Experience training on large-scale multi-node GPU clusters or with training infrastructure and evaluation frameworks
• Published ML research or open-source contributions in relevant areas