BS or higher in Computer Science, Electrical Engineering, Applied Mathematics, or related STEM field.
3+ years of applied machine learning research or development experience.
Strong Python programming skills, with expertise in PyTorch, TensorFlow, or JAX.
Experience building and iterating ML models and pipelines.
Familiarity with data preprocessing, labeling, and exploratory analysis.
Ability to work with multimodal data, including imaging and time series.
Responsibilities
Evaluate and implement machine learning approaches for unique neural data design problems.
Work with real-time, multi-dimensional, multimodal datasets.
Collaborate with neuroscience, hardware, and software teams.
Explore new model architectures and conduct detailed experiments.
Learn neuroimaging and neuroscience context, with support provided.
Benefits
Support for learning neuroimaging and neuroscience context.
Full Job Description
You will:
Critically evaluate and implement the best machine learning approaches for our unique design problems in neural data
Work with real-time, multi-dimensional, multimodal datasets
Collaborate closely with neuroscience, hardware, and software teams to co-design end-to-end systems
Explore new model architectures and perform detailed experimentation and analysis
Learn neuroimaging and neuroscience context (we will support you in getting up to speed)
You have:
An BS or higher in Computer Science, Electrical Engineering, Applied Mathematics, or a related STEM field (exceptional self-taught researchers also considered)
3+ years of applied ML research or development experience, or equivalent depth through publications, projects, or startup work
Strong Python programming skills with experience in PyTorch, TensorFlow, or JAX
Built and iterated quickly on ML models and pipelines
Experience with data preprocessing, labeling, and exploratory analysis
Agility working with multimodal data (e.g., imaging + time series, text + audio)
Proven ability to thrive in small, fast-moving teams
You might also have:
Publications in top ML or domain-specific journals/conferences
Experience with biomedical, neuroimaging, or other high-dimensional sensor data
A background in signal processing for time-series or imaging data
Experience with distributed or large-scale training (e.g., mixed precision, very large datasets)
Knowledge of semi-supervised or self-supervised approaches
Excitement to learn neuroimaging and neuroscience context
