Machine Learning Engineer

Senior Machine Learning Engineer (Foundation Models) Type : Full-time Location : US or UK At this time we are only able to hire candidates who are eligible to work in the US or the UK. The Role We'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. Responsibilities Model development and training 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 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 Research and evaluation Define and run experiments to test modeling choices, and build the evaluation that tells us whether the model is learning what we need Draw on the neuroscience and sequence-modeling literature to inform architecture and training Turn research findings into reproducible, production-quality model code Collaboration 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 our existing ML engineer Requirements Core (essential) You've 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 Solid software fundamentals: Python and PyTorch (or JAX), and the discipline to write reproducible model code Comfort working with large, messy, multimodal or time-series data Pragmatism for an early-stage environment where you own work from end to end Valued Enthusiasm for the science of modeling biological data and the intersection of the brain and AI Familiarity with representation learning and self-supervised or generative modeling Background or strong interest in neuroscience, biosignals, or computational cognitive science Experience with hyperparameter optimization, training infrastructure, or evaluation frameworks Publications or open-source contributions in relevant areas Netholabs Ltd