Asks for

The ad says“Advanced understanding of extracellular neural recordings, spike detection and sorting, sampling theory, filtering, aliasing, referencing, and artifact handling”
The ad says“Advanced understanding of extracellular neural recordings, spike detection and sorting, sampling theory, filtering, aliasing, referencing, and artifact handling”
The ad says“Exceptional programming ability in Python, with strong software design, debugging, profiling, automated testing, and version control practices”
The ad says“Exceptional programming ability in Python, with strong software design, debugging, profiling, automated testing, and version control practices”
The ad says“Advanced understanding of extracellular neural recordings, spike detection and sorting, sampling theory, filtering, aliasing, referencing, and artifact handling”
The ad says“Advanced understanding of extracellular neural recordings, spike detection and sorting, sampling theory, filtering, aliasing, referencing, and artifact handling”
The ad says“Experience with scientific formats and storage systems such as NEV/NSx, NWB, HDF5, and Zarr, including efficient partial reads and metadata management”
The ad says“Hands-on experience with intracortical electrophysiology data and its on-disk representation, including binary layouts, event timestamps, channel metadata, and continuous signals”
The ad says“Experience with scientific formats and storage systems such as NEV/NSx, NWB, HDF5, and Zarr, including efficient partial reads and metadata management”
The ad says“Experience with scientific formats and storage systems such as NEV/NSx, NWB, HDF5, and Zarr, including efficient partial reads and metadata management”
The ad says“Exceptional programming ability in Python, with strong software design, debugging, profiling, automated testing, and version control practices”
The ad says“Exceptional programming ability in Python, with strong software design, debugging, profiling, automated testing, and version control practices”
The ad says“Advanced understanding of extracellular neural recordings, spike detection and sorting, sampling theory, filtering, aliasing, referencing, and artifact handling”
The ad says“Advanced understanding of extracellular neural recordings, spike detection and sorting, sampling theory, filtering, aliasing, referencing, and artifact handling”
The ad says“Exceptional programming ability in Python, with strong software design, debugging, profiling, automated testing, and version control practices”
The ad says“Advanced understanding of extracellular neural recordings, spike detection and sorting, sampling theory, filtering, aliasing, referencing, and artifact handling”
The ad says“Advanced understanding of extracellular neural recordings, spike detection and sorting, sampling theory, filtering, aliasing, referencing, and artifact handling”
The ad says“Exceptional programming ability in Python, with strong software design, debugging, profiling, automated testing, and version control practices”
The ad says“Experience with scientific formats and storage systems such as NEV/NSx, NWB, HDF5, and Zarr, including efficient partial reads and metadata management”

Build the systems that expand human capability

At Blackrock Neurotech, we’ve spent decades making the impossible possible – helping people move, speak, and reconnect with the world when they otherwise could not. We’ve seen that restoring function restores more than ability. It restores independence, identity, and agency.

Today, we are building the next generation of human capability: brain-computer interfaces that are designed to be safe, scalable, and trusted in the real world. Our work is not only about reconnecting people to what was lost, but about expanding what is possible – creating a seamless interface between human intent and technology.

This is foundational work in a category-defining field. You will help build the infrastructure for a future where neural interfaces are invisible, reliable, and deeply human-centered.

Working at Blackrock Neurotech means:

Owning meaningful, high-impact problems at the frontier of science and engineering
Building alongside experienced, thoughtful peers across disciplines
Solving technically complex challenges grounded in real human outcomes
Contributing to a culture that values rigor, clarity, and long-term thinking over noise

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. You will work closely with model researchers to understand their data requirements and translate them into reliable, scalable infrastructure. You will also partner with infrastructure and IT teams to ensure the right storage, processing, and delivery resources are in place. You will take end-to-end technical ownership of a growing intracortical BCI data corpus that sits at the foundation of Blackrock's neural foundation model work. Your work will directly shape the quality, scale, and reliability of the data these models learn from, while giving researchers the confidence and freedom to focus on model development

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.

How We Work

We are a small, experienced team working on consequential problems.

We take ownership of outcomes and follow through with clarity and accountability
We prioritize sustained, high-quality work over performative urgency
We value rigor, sound judgement and thoughtful decision-making
We collaborate deliberately: low ego, high trust and high context

This is a high-ownership role, but it is not an "always-on" one. We expect strong work and our people to have a life outside of it.