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Mid Level Neuromorphic Computing Jobs in California

Tranzeal, Inc.

Java Developer

Sunnyvale, CA · On-site

$60.75 - $78.50/hr

Strong knowledge of building highly scalable systems using cloud and edge computing. Mid-level proficiency in CI/CD, GITOPS, GIT, and Jenkins. Expert-level problem-solving skills with the ability to ...

Tranzeal, Inc.

Java Backend Engineer

Sunnyvale, CA · On-site

$60 - $78/hr

Strong knowledge of building highly scalable systems using cloud and edge computing. Mid-level proficiency in CI/CD, GITOPS, GIT, and Jenkins. Expert-level problem-solving skills with the ability to ...

SAIC

Deskside Support Engineer

Sacramento, CA

$40K - $80K/yr

... as computing, networking, integration, and domain administration. Must possess a degree. High ... Escalation point for Junior and Mid-level desk side engineers. Ability to act independently in a ...

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Mid Level Neuromorphic Computing information

What are mid level neuromorphic computing professionals?

Mid level neuromorphic computing professionals are specialists with several years of experience who design, develop, and optimize hardware and software systems inspired by the structure and function of the human brain. They typically work on building and programming neuromorphic chips, developing algorithms that mimic neural processes, and integrating these systems into real-world applications such as robotics or edge computing. Their expertise bridges neuroscience, computer engineering, and artificial intelligence, and they often collaborate with interdisciplinary teams to advance brain-inspired computing technologies.

What are some common challenges faced by professionals in mid-level neuromorphic computing roles, and how can they be addressed?

Professionals in mid-level neuromorphic computing roles often encounter challenges such as integrating novel hardware with existing software systems, managing the complexity of neural-inspired algorithms, and keeping pace with rapid advancements in the field. Collaborating closely with multidisciplinary teams—including hardware engineers, data scientists, and neuroscientists—can help address these challenges. Additionally, staying updated on the latest research and industry trends, as well as participating in collaborative projects, can enhance problem-solving skills and foster innovation in this evolving area.

What is the difference between Mid Level Neuromorphic Computing vs Mid Level Machine Learning Engineer?

AspectMid Level Neuromorphic ComputingMid Level Machine Learning Engineer
Required CredentialsBachelor's in Computer Science, Electrical Engineering, or related field; knowledge of neuromorphic hardwareBachelor's in Computer Science, Data Science, or related; experience with ML frameworks
Work EnvironmentResearch labs, hardware development, AI hardware companiesTech companies, startups, data-driven organizations
Industry UsageAI hardware, neuromorphic chip design, cognitive computingSoftware development, AI applications, data analysis

Mid Level Neuromorphic Computing professionals focus on hardware and cognitive architectures inspired by the brain, often working with specialized hardware and research teams. In contrast, Mid Level Machine Learning Engineers develop algorithms and models primarily in software to solve data-driven problems. Both roles require a strong technical background but differ in their focus on hardware versus software applications.

What are the key skills and qualifications needed to thrive as a Mid Level Neuromorphic Computing Engineer, and why are they important?

To thrive as a Mid Level Neuromorphic Computing Engineer, you need a solid background in computer engineering, neuroscience, and machine learning, usually supported by a relevant degree and experience with neural network architectures. Familiarity with tools like Python, MATLAB, TensorFlow, and simulation platforms such as NEST or SpiNNaker, along with knowledge of specialized hardware, is typically required. Strong problem-solving, collaboration, and communication skills help you innovate and effectively share complex ideas with multidisciplinary teams. These skills and qualifications are crucial for developing advanced neuromorphic systems that bridge neuroscience and AI, pushing the boundaries of efficient computing.
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Mid Level Neuromorphic Computing jobs near you
Infographic showing various Mid Level Neuromorphic Computing job openings in California as of July 2026, with employment types broken down into 2% Locum Tenens, 86% Full Time, 10% Part Time, 1% Temporary, and 1% Contract. Highlights an 87% Physical, 3% Hybrid, and 10% Remote job distribution.
Intern - Microelectronics Architecture/Hardware Energy Efficiency

Intern - Microelectronics Architecture/Hardware Energy Efficiency

SLAC National Accelerator Laboratory

Menlo Park, CA • On-site

Full-time

Re-posted 19 days ago

Job description

Microelectronics Architecture/Hardware Energy Efficiency Internship
Position Overview:
The summer internship will consist of estimating energy used in computing of hardware systems based on published data and also those in research. The intern should have a background in physics, or electrical or computer engineering and computer science. As part of a new initiative from the Department of Energy (DOE) on Energy Efficient Computing, SLAC is offering summer internships for graduate students at SLAC National Laboratory (& Stanford University).
The internship will focus on estimating energy used in computing of hardware systems based on published data and also those of research systems in laboratories for which data are available. During the internship, the student will work using the basics of computing architecture, memory, interconnects, and hardware to estimate energy requited per operation for different architectures with different implementations of instruction sets and different architectures and processing units like GPU, TPUs etc. This is will be compared with top-down estimates1 for providing more precise bounds on energy required. The systems of relevance will include general purpose von Neumann architectures (CPUs and GPUS) and special purpose architectures (neuromorphic systems), ASICs and FPGAs. The analysis will provide basis to a larger DOE effort currently developing roadmap for energy efficiency in computing. The application of this analysis can cross ML systems such as those used in Natural Language Processing and also hardware used for scientific computing such as Top500 covering applications including machine learning and measurements in areas of science and engineering including Chemistry, Chemical Engineering, Material Science, Fluid Mechanics, Aerospace Engineering, Computer Science, etc.
The objective of this internship is to give students an opportunity to gain valuable hands-on experience by working on real-world problems related to bridging their expertise in hardware with new perspectives in thinking about computing. This experience will not only enhance their skills and knowledge in the field. It will also give them a boost when applying for jobs or graduate programs in the future. The mentor serves as a co-advisor, and interns may have the opportunity to continue their research during the academic year to fulfill a thesis or other academic requirements.
Specific responsibilities (include but are not limited to):
  • Identify the different architectures and hardware including those used in machine learning, scientific computing etc.
  • Identify the instruction
  • Outline the nature and quantity of data required for each of the hardware, in discussion with the mentor.
  • Conduct data analysis to ascertain the quality and verity of data, in partnership with the mentor.
  • Develop models for estimating energy efficiencies for the problems in working with the mentor.
  • Carry out Verification, Validation and critical analysis of their estimates
  • Refine and develop models to integrate scientific knowledge in the model formulation and training phases.
  • Prepare reports and scientific publications outlining the advances under the aegis of the mentor.

Opportunities and Benefits
  • Growth and mentorship from exceptionally talented engineers and scientists from SLAC and Stanford University.
  • A mission-driven, stable, collaborative, highly interdisciplinary, and supportive work environment.
  • Opportunity to experience a multidisciplinary research environment, integrating knowledge from many subject areas spanning computer engineering, physical sciences, applied mathematics, and software applications.

Note: This is an hourly, non-benefits eligible temporary-nonexempt, internship position (work at 50% full-time equivalent or more), not to exceed 980 hours in six consecutive months.
Eligible applicants must be at least 18 years of age, currently enrolled in an educational program or recently graduated, and have US work authorization. The on-site internship program is for a period of eight weeks and takes place between May and Mid-August, with the start date being contingent on the convenience of the candidate.
To be successful in this position, candidates should:
  • Pursuing a Master's or Doctoral degree in a science, engineering or equivalent discipline.
  • Strong communication skills.
  • Ability to work in a collaborative environment.
  • Passionate about innovative solutions for Science & Engineering problems.
  • Some prior experience at Python programming would be beneficial.

SLAC Employee Competencies:
  • Effective Decisions: Uses job knowledge and solid judgment to make quality decisions in a timely manner.
  • Self-Development: Pursues a variety of venues and opportunities to continue learning and developing.
  • Dependability: Can be counted on to deliver results with a sense of personal responsibility for expected outcomes.
  • Initiative: Pursues work and interactions proactively with optimism, positive energy, and motivation to move things forward.
  • Adaptability: Flexes as needed when change occurs, maintains an open outlook while adjusting and accommodating changes.
  • Communication: Ensures effective information flow to various audiences and creates and delivers clear, appropriate written, spoken, presented messages.
  • Relationships: Builds relationships to foster trust, collaboration, and a positive climate to achieve common goals.

Physical requirements and working conditions:
  • Consistent with its obligations under the law, the University will provide reasonable accommodation to any employee with a disability who requires accommodation to perform the essential functions of the job.

Work Standards:
  • Interpersonal Skills: Demonstrates the ability to work well with Stanford colleagues and clients and with external organizations.
  • Promote Culture of Safety: Demonstrates commitment to personal responsibility and value for environment, safety and security; communicates related concerns; uses and promotes safe behaviors based on training and lessons learned. Meets the applicable roles and responsibilities as described in the ESH Manual, Chapter 1General Policy and Responsibilities: http://www-group.slac.stanford.edu/esh/eshmanual/pdfs/ESHch01.pdf
  • Subject to and expected to comply with all applicable University policies and procedures, including but not limited to the personnel policies and other policies found in the University's Administrative Guide, http://adminguide.stanford.edu
  • As a national laboratory, SLAC National Accelerator Laboratory is responsible for adhering to the Homeland Security Presidential Directive 12 (HSPD-12) and Department of Energy (DOE) Order 473.1A, which require employees to obtain and maintain a HSPD-12 Personal Identity Verification (PIV) Credential. To obtain this credential, employees must successfully complete the applicable tier of federal background investigation post hire and receive a favorable federal adjudication. The tier of federal background investigation will be determined by job duties and national security or public trust responsibilities associated with the job. All tiers of investigation include a declaration of illegal drug activities, including use, supply, possession, or manufacture within the last 1 to 7 years (depending on the applicable tier of investigation). Illegal drug activities include marijuana and cannabis derivatives, which are still considered illegal under federal law, regardless of state laws.

  • Classification Title: SLAC Intern Students [Level II - III]
  • Job Code: 0901
  • Employment Duration : 12 weeks

The expected pay range for this position is $31.02 - $39.26 per hour.
SLAC National Accelerator Laboratory/Stanford University provides pay ranges representing its good faith estimate of the salary or hourly wage the university reasonably expects to pay for a position upon hire. The pay offered to a selected candidate will be determined based on factors such as (but not limited to) the scope and responsibilities of the position, the qualifications of the selected candidate, departmental budget availability, internal equity, geographic location, and external market pay for comparable jobs.
At SLAC/Stanford, base pay represents only one aspect of the comprehensive rewards package.

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