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Brain Machine Interface Jobs in California (NOW HIRING)

Subsense

Test Engineer

Palo Alto, CA · On-site

$120K - $140K/yr

... surgical, bidirectional brain-computer interface powered by plasmonic and magnetoelectric ... machine interaction. The Opportunity We are looking for a hands-on Test Engineer to help build ...

Alljoined

Software Engineer

San Francisco, CA · On-site

$140K - $200K/yr

... brain-computer interfaces. You might be a good fit if you * Have 5+ years of production software ... Experience developing internal libraries, SDKs, or tooling that powers machine learning and ...

Merge Labs

Senior Recruiter

San Francisco, CA · On-site

$120K - $200K/yr

Our hiring spans bioengineering, neuroscience, machine learning, engineering, and adjacent scientific and technical disciplines that contribute to building brain-computer interfaces that bridge ...

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How much do brain machine interface jobs pay per hour?

As of Jul 17, 2026, the average hourly pay for brain machine interface in California is $22.52, according to ZipRecruiter salary data. Most workers in this role earn between $19.47 and $25.14 per hour, depending on experience, location, and employer.

What are the career paths in neurolinguistics?

Career paths in neurolinguistics include roles such as research scientist, clinical neuropsychologist, speech-language pathologist, and cognitive neuroscientist. These positions often require advanced degrees like a master's or Ph.D., along with skills in neuroimaging, data analysis, and understanding of language processing in the brain.

Is there a high demand for neuroscientists?

Neuroscientists, including those working on brain-machine interfaces, are in increasing demand due to advancements in neurotechnology and the growing need for research in neural disorders and brain-computer communication. Employment opportunities are expected to grow as interdisciplinary skills in neuroscience, engineering, and data analysis become more valuable in both academia and industry. Certifications in neuroimaging or programming can enhance job prospects in this field.

What are the typical daily responsibilities of someone working in a Brain Machine Interface position?

Professionals in Brain Machine Interface roles usually divide their time between designing experiments, analyzing neural data, developing and testing interface prototypes, and collaborating with cross-disciplinary teams such as neuroscientists, engineers, and clinicians. They may also be involved in writing technical documentation, participating in regulatory compliance activities, and keeping up with the latest scientific literature. Depending on the project, work can flow between laboratory research and computational tasks, requiring flexibility and an eagerness to learn new techniques. The environment is often collaborative and research-focused, providing ample opportunities to contribute to exciting advancements in neurotechnology.

What are the key skills and qualifications needed to thrive in the Brain Machine Interface position, and why are they important?

To thrive in a Brain Machine Interface (BMI) role, you need a strong background in neuroscience, biomedical engineering, computer science, or a related field, often supported by advanced degrees or specialized training. Proficiency with signal processing software (such as MATLAB or Python), brain imaging tools, and hardware prototyping is typically required, along with familiarity with regulatory standards. Strong problem-solving skills, collaboration, and attention to detail help individuals excel in multi-disciplinary teams working at the intersection of biology and technology. These competencies are crucial for developing safe, effective interfaces and driving innovation in this fast-evolving field.

Are BCIs the future?

Brain Machine Interface (BMI) technology is advancing rapidly, with applications in medical rehabilitation, neuroprosthetics, and human-computer interaction. As research progresses, BCIs are expected to become more integrated into healthcare and consumer devices, potentially transforming how humans interact with technology in the future.

What jobs can I get with HCI?

With expertise in Human-Computer Interaction (HCI), you can pursue roles such as UX designer, usability analyst, interaction designer, or research scientist. These jobs often require skills in user-centered design, prototyping tools, and understanding of cognitive psychology or ergonomics.

What is a Brain Machine Interface job?

A Brain Machine Interface (BMI) job involves developing technologies that connect the human brain with computers or external devices. Professionals in this field work on designing, testing, and improving neural interfaces to restore lost sensory or motor functions, enhance cognitive abilities, or enable direct brain communication with machines. Roles may include neuroscientists, engineers, and software developers collaborating to advance BMI applications in healthcare, assistive technology, and neuroprosthetics.

What are the most commonly searched types of Brain Machine Interface jobs in California? The most popular types of Brain Machine Interface jobs in California are:
What are popular job titles related to Brain Machine Interface jobs in California? For Brain Machine Interface jobs in California, the most frequently searched job titles are:
What job categories do people searching Brain Machine Interface jobs in California look for? The top searched job categories for Brain Machine Interface jobs in California are:
What cities in California are hiring for Brain Machine Interface jobs? Cities in California with the most Brain Machine Interface job openings:
Brain Machine Interface jobs near you
Infographic showing various Brain Machine Interface job openings in California as of July 2026, with employment types broken down into 5% As Needed, 64% Full Time, 26% Part Time, 2% Temporary, and 3% Contract. Highlights an 97% Physical, and 3% Remote job distribution, with an average salary of $46,846 per year, or $22.5 per hour.
Director, Machine Learning, Alzheimer's Disease Initiative

Director, Machine Learning, Alzheimer's Disease Initiative

Arc Institute

Palo Alto, CA • On-site

Full-time

Posted 7 days ago

Job description

About Arc Institute
Arc Institute is an independent nonprofit research organization at the interface of artificial intelligence and biology, working to accelerate scientific progress and understand the root causes of complex diseases. Founded in 2021 and based in Palo Alto, Arc partners with Stanford University, UC Berkeley, and UC San Francisco.
Unlike academia, our scientists have long-term funding and industry-like resources. Unlike industry, they're free to pursue high-risk, long-term research without commercial pressures. Arc's Technology Centers and Core Investigator labs work side by side, integrating experimental and computational biology under one roof to tackle problems neither could solve alone.
Our two Institute Initiatives reflect this model in action:
  • Virtual Cell Initiative: Building a full-stack virtual cell model to identify disease mechanisms and nominate drug targets, accelerating the path from biological insight to clinical trials.
  • Alzheimer's Disease Initiative: Mapping the genes, pathways, and environmental factors behind Alzheimer's disease to develop drug candidates that address root causes.

More than 300 Arconauts work together at our Palo Alto headquarters, backed by substantial long-term philanthropic funding.
About the Position
We are searching for an exceptional scientific leader to establish a new team within Arc Institute's Computational Technology Center, serving as the Director, Machine Learning for our Alzheimer's Disease Initiative (ADI).
This ambitious initiative spans Arc's Technology Centers and Core Investigator Laboratories and focuses on high-throughput interrogation of neurodegeneration and Alzheimer's disease mechanisms using advanced gene editing and functional genomics approaches. As the Machine Learning Research Lead, ADI, you will spearhead development of sophisticated machine learning foundation models to capture cell states and infer gene regulatory networks and causal relationships to predict therapeutic interventions.
This position offers the rare opportunity to build and lead a world-class team while making direct contributions to understanding and potentially treating Alzheimer's disease through state-of-the-art computational biology and machine learning approaches.
About You
  • You are passionate about machine learning and computational biology, with expertise in applying cutting edge ML approaches to biological systems
  • You excel at developing interpretable machine learning approaches, such as variational inference and causal modeling methods
  • You are excited about building and leading a technical team while remaining hands-on with foundation model development and implementation.
  • You thrive in collaborative, multidisciplinary environments and enjoy working with both computational scientists and wet lab biologists
  • You are a continuous learner who stays current with the latest developments, in both machine learning and neuroscience
In This Position, You Will
  • Attract, build and lead a team of exceptional machine learning research scientists dedicated to developing foundation models for cellular systems in Alzheimer's disease
  • Develop and execute on a roadmap of interpretable machine learning approaches to understand disease mechanisms, with emphasis on variational inference, causal modeling, as well as modern transformer- and diffusion-based architectures
  • Work closely with experimentalists on brain organoid/spheroid cellular models as well as in vivo models, working with scRNA-seq, Perturb-seq and other datasets to unravel causal gene pathways relevant to Alzheimer's disease
  • Develop predictive modeling approaches to identify how perturbations can move cell states from high risk Alzheimer's profiles back to healthy / low risk states
  • Collaborate closely with experimental biologists to ensure ML models are grounded in disease biology and can feedback into future experimental strategies
  • Foster collaborations with external partners in the computational biology and neuroscience communities
  • Publish high-impact research through preprints, journal publications, open source code, and presentations at leading conferences
Required Qualifications
  • PhD in Computational Biology, Bioinformatics, Machine Learning, Computer Science, or related quantitative field
  • 7+ years of relevant experience with a minimum of 3 years of people management experience
  • Strong research background with experience in academic settings (university, research institute) and/or biotech/pharmaceutical industry with a focus on scientific innovation
  • Proven expertise in machine learning applications to biological datasets, with specific experience in single-cell profiling data and foundation model development
  • Deep experience with interpretable machine learning approaches for biological systems (e.g. variational inference methods).
  • Advanced technical skills in machine learning frameworks, particularly PyTorch, and ideally experience with model training at scale
  • Publications in top-tier journals in computational biology and machine learning
  • Excellent communication skills with ability to present complex machine learning concepts to both computational and biological audiences
  • Proven ability to remain technically hands-on while providing effective team leadership, mentorship, and management
  • Background in neurodegeneration research including familiarity with Alzheimer's disease datasets, pathways, networks, disease mechanisms, and eQTL analysis is a plus

The base salary range for this position is $380,000-$420,000. These amounts reflect the range of base salary that the Institute reasonably would expect to pay a new hire or internal candidate for this position. The actual base compensation paid to any individual for this position may vary depending on factors such as experience, market conditions, education/training, skill level, and whether the compensation is internally equitable, and does not include bonuses, commissions, differential pay, other forms of compensation, or benefits. This position is also eligible to receive an annual discretionary bonus, with the amount dependent on individual and institute performance factors.

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