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Neural Interface Research Jobs (NOW HIRING)

Neuralink

Machine Learning Engineer

Austin, TX

You will have access to the most cutting-edge neural interface hardware and develop ... Formulate research questions to guide the development of neural networks and signal processing ...

Delsys

R&D Software Engineer (FT)

Natick, MA · On-site

$90K - $110K/yr

... neural interface technologies. The position requires expertise in developing signal processing and ... Successful candidates will collaborate closely with developers, R&D engineers and scientists ...

Neuralink

Machine Learning Engineer

Austin, TX · On-site

$199K - $331K/yr

You will have access to the most cutting-edge neural interface hardware and develop ... Formulate research questions to guide the development of neural networks and signal processing ...

Neuralink

Machine Learning Engineer

Austin, TX · On-site

$199K - $331K/yr

You will have access to the most cutting-edge neural interface hardware and develop ... Formulate research questions to guide the development of neural networks and signal processing ...

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Neural Interface Research information

What is neural interface research?

Neural interface research is the scientific study and development of technologies that connect the nervous system, particularly the brain, with external devices or computers. These interfaces, often called brain-computer interfaces (BCIs) or neural prosthetics, enable direct communication between neural tissue and electronic systems. The goal of this research is to restore lost sensory or motor functions, treat neurological disorders, or enhance human capabilities. Neural interface research is highly interdisciplinary, involving neuroscience, engineering, computer science, and medicine. Advances in this field have the potential to revolutionize healthcare and human-machine interaction.

What are the key skills and qualifications needed to thrive in neural interface research?

To thrive in Neural Interface Research, you need advanced knowledge in neuroscience, biomedical engineering, and signal processing, often supported by a graduate degree in a related field. Proficiency with programming languages (such as Python or MATLAB), neural data acquisition systems, and simulation tools is typically required. Exceptional problem-solving abilities, collaboration, and strong communication skills help researchers innovate and translate findings across multidisciplinary teams. These skills are crucial for developing cutting-edge neural technologies and ensuring rigorous, impactful scientific progress.

What are some common interdisciplinary challenges faced by professionals in neural interface research teams?

Neural Interface Research teams often bring together experts from neuroscience, engineering, computer science, and clinical backgrounds, which can lead to challenges in communication and aligning goals across disciplines. Collaborators may use different terminology or have varying expectations regarding project timelines and outcomes. Successful professionals in this field need to be proactive in fostering clear communication, demonstrating adaptability, and developing a basic understanding of adjacent fields to effectively contribute to collaborative projects. These interdisciplinary challenges ultimately offer valuable opportunities for personal growth and innovation.

What is the difference between Neural Interface Research vs Neural Engineering?

AspectNeural Interface ResearchNeural Engineering
Required CredentialsAdvanced degrees in neuroscience, biomedical engineering, or related fieldsSimilar credentials, often with additional focus on device design and implementation
Work EnvironmentResearch labs, universities, biotech companiesResearch labs, medical device companies, clinical settings
Industry UsageFocuses on developing and understanding neural interfacesDesigning, testing, and applying neural interface devices
Common Search IntentResearch methods, latest advancements, academic rolesProduct development, device engineering, clinical applications

Neural Interface Research primarily involves exploring and understanding neural interfaces through scientific investigation, while Neural Engineering focuses on designing and developing neural interface devices for practical use. Both roles require similar educational backgrounds but differ in their application and work environment.

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Neural Interface Research jobs near you
Infographic showing various Neural Interface Research job openings in the United States as of August 2026, with employment types broken down into 1% Internship, 1% As Needed, 85% Full Time, 11% Part Time, and 2% Contract. Highlights an 84% Physical, 4% Hybrid, and 12% Remote job distribution.

BCI Software Team Lead

BlackRock Neurotech

Salt Lake City, UT • On-site

Full-time

Re-posted 17 days ago

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.
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
This role exists to translate Blackrock Neurotech's brain-computer interface research into reliable, regulated, real-time software that reaches users. The Software Team Lead sits at the critical boundary between experimental neural data processing and production-grade, IEC 62304-compliant systems, ensuring that novel concepts in decoding and signal interpretation are transformed into safe, scalable, and maintainable software.
You will lead a team of 5-8 engineers while also contributing hands-on code, setting technical direction, and owning the architecture of the platforms that acquire, process, and interpret real-time neural and physiological signals. You will partner closely with neuroscience, firmware, hardware, clinical, and regulatory teams to ensure software accelerates rather than constrains the path from lab to patient.
Success In This Role Looks Like
  • Establishing a stable, high-trust engineering team with clear ownership, technical direction, and growth paths
  • Delivering a documented, defensible software architecture aligned with product, research, and regulatory requirements
  • Driving measurable improvements in real-time system performance, reliability, and cybersecurity posture
  • Consistently meeting milestones and shipping production-ready software aligned with clinical and regulatory needs

You will operate with meaningful ownership in a high-consequence environment, contributing to systems that must be precise, reliable, and durable.
The Impact You'll Make
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.
You will:
  • Build and shape real-time brain-computer interface software systems that move research into clinical and patient-facing applications
  • Directly influence how people interact with critical neurotechnology that restores communication, mobility, and independence
  • Contribute to work that improves lives through safer, more capable, and more reliable BCI systems that patients depend on daily
What You'll Do
The Software Team Lead is a hybrid leadership and hands-on engineering role, spending time both guiding a team of 5-8 engineers and directly contributing to the codebase. You'll move fluidly between architecture design, technical decision-making, regulatory documentation, and day-to-day engineering work, while also owning core people leadership responsibilities such as hiring, onboarding, performance management, goal-setting, and career development.
You will own the technical direction and architecture of Blackrock Neurotech's BCI application software used across preclinical research, clinical trials, and future medical devices. This means you are responsible for how the platform acquires, processes, and interprets real-time neural and physiological data, and for ensuring it remains scalable, performant, reliable, and compliant as the system grows and moves toward regulated deployment.
On a day-to-day basis, you'll work on problems at the intersection of real-time, embedded, and regulated medical-device software while collaborating across multiple disciplines to turn complex neuroscience and engineering challenges into shipped systems.
  • Lead and mentor a team of 5-8 engineers while actively contributing to the codebase
  • Own system architecture and technical direction for real-time BCI application software
  • Design and maintain low-latency, high-reliability streaming signal-processing pipelines
  • Ensure deterministic performance across CPU, GPU, and/or FPGA-accelerated components
  • Strengthen cybersecurity and reliability of connected medical-device software systems
  • Define and enforce engineering standards, design review processes, and best practices
  • Drive software readiness for clinical studies, trials, and regulated device environments
  • Balance research prototyping with production-grade software development and long-term maintainability
  • Partner closely with neuroscientists on signal processing and algorithm requirements
  • Collaborate with hardware and firmware teams on system integration and performance
  • Work with clinical teams on study design, trial software needs, and deployment readiness
  • Coordinate with Regulatory and Quality teams on documentation, traceability, and verification requirements
What You Bring
Minimum Qualifications
Experience & Domain
  • 10+ years combined education and experience in software engineering, with at least 3 years building production software in the field
  • 3+ years leading or managing engineering teams, with demonstrated success running teams through hiring, mentorship, goal-setting, and performance management
  • Proven track record of shipping production software in real-time, embedded, or safety-critical domains
  • Experience developing software for, or within, a regulated environment (e.g., for medical device, ISO 13485, FDA QSR/QMSR, or equivalent)

Technical Competencies
  • Demonstrated ability to define and communicate software architecture for complex, multi-component systems to both technical and non-technical stakeholders
  • Strong command of real-time architectures, data streaming, and signal processing, including determinism, latency constraints, and resource management
  • Proficiency in real-time and/or embedded software development
  • Hands-on proficiency in Python for data pipelines, test automation, and prototyping
  • Working experience with high-performance compute for low-latency signal processing using GPU (Metal, CUDA, OpenCL), FPGA, or equivalent
  • Familiarity with cybersecurity principles for networked or connected medical device software (threat modeling, secure coding practices, vulnerability management)

Behaviors & Attributes
  • Ownership mindset that takes accountability for individual, team, and department outcomes and follows problems through to resolution
  • Leads by example, balancing hands-on technical contribution with mentorship and people leadership rather than choosing one over the other
  • Sound technical judgment under ambiguity, with the discernment to know when to invest in productization versus prototype and when to escalate
  • Curiosity and adaptability through active tracking of advances in real-time systems and neurotechnology and adjusts approach as the company, products, and regulatory landscape evolve
  • Excellent communicator and cross-functional coordinator across neuroscience, hardware, firmware, clinical, regulatory, and quality partners

Preferred Qualifications
We're especially interested in candidates who have operated at the intersection of real-time systems, hardware integration, and regulated medical software environments. Ideal backgrounds combine deep technical systems expertise with experience translating complex research or clinical requirements into reliable, production-grade software in fast-moving settings.
Domain Expertise
  • Prior experience building software for brain-computer interfaces, neural recording systems, or closed-loop neurostimulation
  • Deep understanding of real-time and multi-threaded systems, including OS-, scheduler-, or kernel-level performance tuning and debugging
  • Strong systems thinking with proven ability to tightly integrate software with hardware, firmware, and clinical workflows

Regulated and clinical environment experience
  • Experience supporting FDA submissions (IDE, 510(k), De Novo) in a technical or engineering leadership role
  • Familiarity with IEC 62304 and design-controlled development, including authoring or reviewing software lifecycle and traceability artifacts
  • Experience working within clinical study or trial environments, including software deployment to sites, protocol alignment, and safety reporting

Environment and stage fit
  • Experience in startup, scale-up, or deep-tech environments with rapidly shifting priorities and lightweight process structure
  • Proven ability to balance research prototyping with disciplined productization, taking ideas from early exploration to regulated, shipped systems without losing technical intent

Differentiators
  • Track record of mentoring engineers and building high-trust, inclusive teams that perform under pressure and through ambiguity
  • Open-source contributions, publications, or conference talks in real-time systems, neurotechnology, signal processing, or medical device software
  • Background in neuroscience, biomedical engineering, or related fields enabling fluent collaboration with scientific and clinical partners
  • Experience hiring and scaling engineering teams through rapid growth or regulatory transition periods
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.

Apply