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Neural Data Infrastructure Engineer

Job in Salt Lake City, Salt Lake County, Utah, 84193, USA
Listing for: Blackrock-Neurotech
Full Time position
Listed on 2026-09-23
Job specializations:
  • IT/Tech
    Data Engineering, Systems Engineer
Salary/Wage Range or Industry Benchmark: 130000 - 190000 USD Yearly USD 130000.00 190000.00 YEAR
Job Description & How to Apply Below

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,…
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