AI Team Lead Job at Blackrock Neurotech (Salt Lake City)

Job Description

As the AI Team/ML Research Lead at Blackrock, you will define and drive the next generation of machine learning systems powering brain-computer interface (BCI) technology This role exists to translate cutting-edge AI research into real-world, high-impact systems capable of decoding complex neural signals in real time. You’ll lead both the technical direction and a team of researchers, shaping architectures that learn robust, generalizable representations from highly complex and heterogeneous brain data.

Job Responsibility

Lead the design, development, and deployment of advanced AI/ML systems for brain-computer interface applications
Drive technical strategy and architecture decisions for complex, large-scale machine learning systems
Build and mentor a high-performing AI/ML research team, fostering a culture of curiosity, rigor, and ownership
Establish and execute roadmaps for AI/ML development, aligning with cross-functional priorities
Translate cutting-edge research into scalable, real-time neural decoding systems
Collaborate cross-functionally with neuroscience, hardware, data, and clinical teams
Communicate critical dependencies, risks, milestones, and key technical decisions to stakeholders
Continuously push the boundaries of model performance, scalability, and real-world applicability

Requirements

8+ years of experience in AI/ML, or PhD in a related field with 3+ years of applied experience
3+ years of experience leading research or applied machine learning teams
Deep expertise in sequence modeling architectures, cross-attention mechanisms, and modern training paradigms
Strong understanding of AI scaling laws, including designing and executing experiments to inform architecture decisions
Proficiency with modern ML tooling, infrastructure, and compute systems
Excellent communication skills with the ability to translate complex technical concepts across audiences

Nice to have

Experience in neural signal processing, brain-computer interfaces, or bio signal decoding
Background in cross-domain transfer learning or multi-task learning
Strong intuition for balancing rapid experimentation with long-term architectural decisions
Experience with model optimization techniques such as distillation, quantization, and edge deployment
Familiarity with regulatory considerations for AI and software in healthcare environments
Proven ability to attract, mentor, and retain top ML talent in a high-autonomy, high-performance environment
Passion for building AI systems that directly improve patient outcomes

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