Machine Learning Researcher

About the companyOrbit is building the foundational AI Infrastructure for emotions. A read interface to human emotions will be the most consequential data layer, enabling empathetic AI and precision mental healthcare. It will fundamentally transform how we interact with each other and technology. Backed by founders and operators of top AI labs, consumer hardware, pharma, and enterprise companies, and venture-backed. About the team we are buildingWe’re building a generational founding team which is truly full-stack - from neural sensors to complex models. If you want to work on deep technological problems and help pioneer the future of NeuroAI, this is the place for you. Projects have opportunities for a high degree of autonomy and demand intense, fast-paced learning. About youStrong Python programming ability with a track record of building and iterating quickly on ML modelsSkilled at designing robust evaluation pipelines and benchmarks for novel architecturesComfortable working with large, noisy, or unconventional datasetsExperience with data preprocessing, labeling, and exploratory analysisFamiliarity with multi-modal architectures and integrating heterogeneous data sourcesExcited to learn neuroimaging and neuroscience context (we will support you in getting up to speed)Preferred Qualifications/ExperienceMS or higher in Computer Science, Electrical Engineering, Applied Mathematics, or related STEM field; exceptional self-taught researchers also considered3+ years of applied ML research or development experience, or equivalent depth through publications, projects, or startup workPublications in top ML or domain-specific journals/conferencesPrior work with multi-modal data (e.g., imaging + time-series, text + audio, etc.)Experience designing and running experiments with novel model architecturesFamiliarity with PyTorch, TensorFlow, or JAXHands-on experience in startups, small research groups, or similarly fast-moving environmentsNice-to-haveExperience with biomedical, neuroimaging, or other high-dimensional sensor dataBackground in signal processing for time-series or imaging dataKnowledge of model training at scale (distributed, mixed precision, large datasets)Experience with semi-supervised or self-supervised approaches Apply to this Job