AI/ML Research Engineer

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

This role exists to advance the core modeling systems that power Blackrock Neurotech’s brain-computer interface decoding technology. As our neural datasets grow across subjects, recording configurations, and tasks, we need an engineer who can build models that learn rich, generalizable representations from heterogeneous brain signals—models that transfer across BCI applications rather than being rebuilt for each one. This role is critical to expanding the capabilities, performance, and scalability of the systems that help restore communication, mobility, and independence for people with neurological conditions.

Success In This Role Looks Like

• Establishing a reproducible training pipeline for large-scale neural modeling and experimentation
• Delivering credible scaling, pretraining, and ablation experiments that guide future modeling decisions
• Producing model artifacts that measurably improve performance on downstream decoding benchmarks
• Building strong partnerships with application-facing teams to ensure modeling work translates into real BCI impact

This is not incremental work. You will help define how humans interact with technology for decades to come. It requires sound judgment, technical depth, and a commitment to getting important things right.

• Build and shape the foundational AI/ML systems that power next-generation brain-computer interfaces
• Advance how people interact with critical assistive technology through more capable and adaptive decoding systems
• Contribute to work that improves lives by enabling greater independence for people living with neurological conditions
• Raise the ceiling for future applications by creating reusable models that compound value across the product ecosystem
• You will operate with meaningful ownership in a high-consequence environment, contributing to systems that must be precise, reliable, and durable.

• You will 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.
• Day-to-day, you'll 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.
• You will shape initiatives spanning dataset curation, training infrastructure, model architecture, and evaluation methodology, with room to lead specific experimental threads as you build context.
• You'll work most closely with other members of the AI/ML team on shared infrastructure and modeling decisions, partner with the data team on dataset pipelines and quality, collaborate with neuroscience colleagues to ground modeling choices in what we know about neural signals, and engage with application-facing engineers so your work meets real downstream needs. The balance shifts over time: more tactical execution early on as you build context, more strategic contribution as you develop a point of view on where the modeling work should go next.

Minimum Qualifications

We’re looking for someone with a strong foundation in modern machine learning and the engineering discipline to apply it at scale. You should bring hands-on experience building and training deep learning models, designing thoughtful experiments, and translating findings into real-world improvements. Success in this role requires strong technical judgment, comfort working with messy and heterogeneous data, and the ability to collaborate effectively across engineering, neuroscience, and clinical teams. Just as important, you take ownership, communicate clearly, and are motivated by the mission to build technology that improves patients’ lives.

• 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 solvingPreferred Qualifications

Candidates who bring any of the following may ramp faster and create additional leverage for the team, though none are required. We value experience applying advanced machine learning to complex real-world signals, operating in high-accountability environments, and collaborating across disciplines. Backgrounds that combine technical depth with domain fluency—especially in neuroscience, healthcare, or other deep-tech settings—can be especially valuable in this role.

• 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

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