Machine Learning Engineer

Senior Machine Learning Engineer (Foundation Models)Type: Full-timeLocation: San Francisco, United States. In person.At this time we are only able to hire candidates who are already based in the US and able to work in-person in San Francisco. We do not support relocation at this time.PLEASE DO NOT USE AI IN YOUR APPLICATION.The RoleWe're building a foundation model of the brain and behavior across species, trained on large-scale multimodal neural and behavioral data, and this role is central to designing and training it. You'll work on the core generative model: architecture, training at scale, and representation learning across neural signals and behavior, along with the research questions that come with modeling biological data as sequences. You'll join a small team and work alongside our existing ML engineer, with room to shape the modeling direction as we grow. This is early-stage scope, so you'll train greenfield models, own parts of the stack, and see your work define the company's core asset.ResponsibilitiesModel development and trainingDesign, train, and iterate on large generative (recurrent or transformer-based) models over multimodal neural and behavioral dataOwn training at scale: data loading, distributed training, hyperparameter optimization, and evaluationDevelop representations that capture structure across species and modalitiesTrain models on animal and human behavioral data as well as direct neural dataResearch and evaluationDefine and run experiments to test modeling choices, and build the evaluation that tells us whether the model is learning what we needDraw on the neuroscience and sequence-modeling literature to inform architecture and trainingTurn research findings into reproducible, production-quality model codeCollaborationPartner with the data engineering team on data readiness and with the research team on what the model needs to captureContribute to the shared modeling roadmap alongside our existing ML engineerRequirementsCore (essential)You've trained large deep learning models end to end, in production or research settingsHands-on experience training transformer or other large sequence models, including distributed training and scalingSolid software fundamentals: Python and PyTorch (or JAX), and the discipline to write reproducible model codeComfort working with large, messy, multimodal or time-series dataPragmatism for an early-stage environment where you own work from end to endValuedEnthusiasm for the science of modeling biological data and the intersection of the brain and AIFamiliarity with representation learning and self-supervised or generative modelingBackground or strong interest in neuroscience, biosignals, or computational cognitive scienceExperience with hyperparameter optimization, training infrastructure, or evaluation frameworksPublications or open-source contributions in relevant areas