Neural Data Infrastructure Engineer

The Role

The Neural Data Infrastructure Engineer will own the systems that turn intracortical recordings into reliable, training-ready data for AI/ML models. You will build the data infrastructure needed to meaningfully scale model capability while preserving the scientific meaning of every recording. As a hands-on individual contributor on a small research team, you will be the technical authority on the data lifecycle from upload through training consumption.

What You'll Do

• Own the design, implementation, and operation of the neural data pipeline from upload and validation through preprocessing, dataset releases, and training data loaders
• Build reliable ingestion for heterogeneous recording formats, preserving raw data, acquisition metadata, channel and electrode mappings, timestamps, units, and behavioral alignment
• Implement and validate neural signal processing for spike events and waveforms, including detection or sorting where needed, quality assessment, binning, and normalization; extend the pipeline to field potentials as the program evolves
• Establish automated checks for corrupt files, missing channels, clock drift, artifacts, recording discontinuities, and inconsistent metadata, with clear criteria for quarantine and recovery
• Design storage layouts, indexing, chunking, compression, and caching that support efficient access to large neural datasets across local and cloud resources
• Create reproducible, versioned datasets with traceable transformations, provenance, and subject and session partitions that prevent leakage between training and evaluation
• Partner with model researchers to define input representations, sequence construction, masks, sampling policies, and interfaces that preserve neural meaning and meet architecture requirements
• Build and profile parallel preprocessing and streaming data loaders, partnering with the training performance engineer to keep GPUs supplied as training scales
• Work with infrastructure, IT, and data owners to plan capacity, access controls, retention, backup, and recovery appropriate to the recordings and their approved uses
• Establish testing, monitoring, documentation, and operational practices that make pipeline failures diagnosable and dataset releases dependable
• Communicate data quality, coverage, processing tradeoffs, and resource needs clearly, and translate evolving research requirements into maintainable engineering plans

What You Bring

• Bachelor's degree in Computer Science, Electrical Engineering, Biomedical Engineering, Neuroscience, or a related technical field, or equivalent practical experience with 3+ years of relevant industry experience OR Master's degree with 1+ years of relevant industry experience OR PhD in a related field
• Demonstrated experience independently designing and operating large-scale scientific data pipelines from end to end, with ownership of software quality, reliability, and delivery
• Exceptional programming ability in Python, with strong software design, debugging, profiling, automated testing, and version control practices
• Advanced understanding of extracellular neural recordings, spike detection and sorting, sampling theory, filtering, aliasing, referencing, and artifact handling
• Hands-on experience with intracortical electrophysiology data and its on-disk representation, including binary layouts, event timestamps, channel metadata, and continuous signals
• Ability to reason about how preprocessing choices alter neural information and affect model training, evaluation, and reproducibility
• Experience with scientific formats and storage systems such as NEV/NSx, NWB, HDF5, and Zarr, including efficient partial reads and metadata management
• Experience with distributed or parallel processing, object storage, workflow orchestration, and restartable jobs at scale preferred
• Familiarity with deep learning models and architectures, including how data processing decisions affect model training, inference, and evaluation
• Strong judgment about dataset lineage, validation, access permissions, and the handling of sensitive research data
• Ability to work across neuroscience, machine learning, and infrastructure teams and bring clarity to ambiguous requirements
• Experience processing local field potentials, synchronizing neural and behavioral streams, or supporting longitudinal neural datasets strongly preferred
• Experience with compiled languages, high-throughput I/O, cloud cost management, or performance-sensitive scientific computing preferred
• Fluency with deep learning data interfaces, including tensor shapes, batching, masking, sampling, and PyTorch preferred

Working Location

This is an on-site role based at Blackrock Neurotech's headquarters in Salt Lake City, Utah. Occasional travel may be required.