Machine Learning Engineer

Netholabs•San Francisco, CA

1d•Onsite

About The Position

The Senior Machine Learning Engineer will help build a foundation model of brain and behavior across species using large-scale multimodal neural and behavioral data, including EEG, MEG, and fMRI. The role centers on the core generative model, including its architecture, large-scale training, and representation learning across neural signals and behavior, as well as research into modeling biological data as sequences. The position is part of a small team and works alongside an existing ML engineer, with scope to shape the modeling direction as the company grows. It involves training greenfield models, owning components of the modeling stack, and contributing to the company's core asset. Candidates must already be based in the United States and be able to work in person in San Francisco; relocation is not supported.

Requirements

Experience training large deep learning models end to end in production or research settings.
Hands-on experience training transformers or other large sequence models, including distributed training and scaling.
Proficiency in Python and PyTorch, clean and concise coding practices, and the discipline to write reproducible model code.
Strong implementation and prototyping skills, with comfort working across research and engineering at scale.
Comfort working with large, messy, multimodal, or time-series data.
Ability to learn new domains quickly, navigate academic literature, and implement new ideas.
An organized, methodical approach to research, with excellent communication and collaboration skills.
Pragmatism suited to an early-stage environment, including owning work end to end.
Already based in the United States and able to work in person in San Francisco.

Nice To Haves

Enthusiasm for modeling biological data, the intersection of brain science and AI, and foundation models for neural data such as EEG, MEG, and fMRI.
Experience with representation learning and self-supervised or generative modeling, including methods such as VAEs, GANs, diffusion models, and contrastive learning.
A background or strong interest in neuroscience, biosignals, or computational cognitive science.
Familiarity with signal processing, especially for EEG, and time-series analysis.
Experience training on large-scale, multi-node GPU clusters, or with hyperparameter optimization, training infrastructure, or evaluation frameworks.
Experience contributing to large existing codebases and ramping up quickly.
Published machine learning research in well-respected venues or open-source contributions in relevant areas.

Responsibilities

Design, train, and iterate on large generative recurrent or transformer-based models over multimodal neural and behavioral data.
Own training at scale, including data loading, distributed multi-node training, hyperparameter optimization, and evaluation.
Develop representations that capture structure across species and modalities.
Train models on animal and human behavioral data, as well as direct neural data.
Take ownership of distinct components of the modeling stack and deliver them as working, well-documented modules.
Define and run experiments to test modeling choices, and build evaluations to determine whether the model is learning what is needed.
Use neuroscience and sequence-modeling literature to inform architecture and training, adapting ideas from areas such as vision, audio, and language to EEG and other neural signals.
Rapidly prototype ideas and turn research findings into reproducible, production-quality model code.
Partner with the data engineering team on data readiness and with the research team on what the model needs to capture.
Contribute to the shared modeling roadmap alongside the existing ML engineer.
Clearly communicate process, results, and trade-offs to technical and scientific colleagues.