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AI/ML Research Engineer

United States, Salt Lake City Employment contract · Job Posted May 17, 2026
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Job Description

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.

Job Responsibility

  • Own substantial pieces of our core modeling work end-to-end, from preparing and curating large neural datasets to designing and running training experiments to analyzing results and turning findings into the next round of model improvements
  • Write and review model and pipeline code, launch and monitor training runs, debug issues that surface at scale, and analyze results to understand not just whether a model works but why
  • Shape initiatives spanning dataset curation, training infrastructure, model architecture, and evaluation methodology, with room to lead specific experimental threads as you build context

Requirements

  • 5+ years of hands-on experience building and training deep learning models, or a PhD in Machine Learning, Computer Science, Computational Neuroscience, or related field with applied industry experience
  • Strong experience with PyTorch (or similar modern ML frameworks) and fluency in Python
  • Solid software engineering practices including version control, testing, code review, and reproducibility
  • Experience designing model architectures and understanding training dynamics, optimization, and compute tradeoffs at scale
  • Ability to design clean experiments, analyze results rigorously, and make data-driven decisions
  • Comfortable working in ambiguous, research-oriented environments with imperfect or evolving datasets
  • Strong written and verbal communication skills across technical and non-technical stakeholders
  • Demonstrated ownership, follow-through, and intellectual honesty in problem solving

Nice to have

  • Experience with neural signal processing, brain-computer interfaces, electrophysiology, or other biosignal domains
  • Relevant adjacent experience in speech, audio, time-series modeling, or multimodal learning
  • Experience with self-supervised learning, representation learning, transfer learning, or multi-task learning
  • Hands-on experience training models at scale using distributed systems, multi-GPU or multi-node environments
  • Familiarity with mixed precision training, gradient checkpointing, and managing long-running training jobs
  • Knowledge of model efficiency techniques such as distillation, quantization, pruning, or edge deployment
  • Experience in regulated or safety-critical environments such as medical devices, healthcare AI, or other deep-tech industries
  • Experience in fast-moving or early-stage environments balancing research ambition with execution discipline
  • Open-source contributions, published research, or other evidence of strong technical work shared publicly
  • Experience partnering with neuroscientists, clinicians, or other domain experts and translating across disciplines

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