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Neuroscience Research Engineer Jobs (NOW HIRING)

Princeton University

Research Specialist I

Princeton, NJ · On-site

$41K - $60K/yr

... neuroscience. The successful applicant will be an energetic learner with strong programming abilities and an interest in the lab's research. For more information about the lab and its research ...

Princeton University

Research Specialist

Princeton, NJ · On-site

$41K - $60K/yr

Prior neuroscience research experience * Programming & computational data analysis experience This position is subject to the University's background check policy. Princeton University is an Equal ...

Princeton University

Research Specialist I

Princeton, NJ · On-site

$41K - $60K/yr

... neuroscience. The successful applicant will be an energetic learner with strong programming abilities and an interest in the lab's research. For more information about the lab and its research ...

Princeton University

Research Specialist

Princeton, NJ

$41K - $60K/yr

Prior neuroscience research experience * Programming & computational data analysis experience This position is subject to the University's background check policy. Princeton University is an Equal ...

Princeton University

Research Specialist

Princeton, NJ · On-site

$41K - $60K/yr

Prior neuroscience research experience * Programming & computational data analysis experience This position is subject to the University's background check policy. Princeton University is an Equal ...

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All JobsNeuroscience Research Engineer Jobs

Neuroscience Research Engineer information

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$37K

$106K

$142.5K

How much do neuroscience research engineer jobs pay per year?

As of Jul 24, 2026, the average yearly pay for neuroscience research engineer in the United States is $106,012.00, according to ZipRecruiter salary data. Most workers in this role earn between $104,000.00 and $104,000.00 per year, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as a Neuroscience Research Engineer, and why are they important?

To thrive as a Neuroscience Research Engineer, you need a strong background in neuroscience, engineering, and data analysis, usually supported by an advanced degree in a relevant field. Familiarity with neuroimaging tools (such as fMRI or EEG), programming languages (like Python or MATLAB), and data processing software is essential, and certifications in research ethics or neurotechnology are advantageous. Outstanding problem-solving abilities, attention to detail, and effective collaboration and communication skills are crucial soft skills. These skills and qualifications enable accurate experimental design, reliable data interpretation, and productive teamwork in advancing neuroscience research.

What does a Neuroscience Research Engineer do?

A Neuroscience Research Engineer applies engineering principles and technical skills to support neuroscience research. They design, develop, and maintain hardware and software tools used in experiments that investigate the brain and nervous system. Their work may involve data acquisition, signal processing, computational modeling, and creating custom devices or algorithms to analyze neural data. These engineers often collaborate closely with neuroscientists to advance research in areas such as brain-computer interfaces, neural imaging, and electrophysiology.

What is the difference between Neuroscience Research Engineer vs Neuroscientist?

AspectNeuroscience Research EngineerNeuroscientist
Required CredentialsBachelor's or Master's in Engineering, Neuroscience, or related fields; often some research experiencePh.D. in Neuroscience or related disciplines; extensive research background
Work EnvironmentResearch labs, tech companies, healthcare settings focusing on developing neurotechnologiesAcademic labs, research institutions, hospitals studying brain functions
Employer & Industry UsageTech firms, biotech companies, medical device manufacturersUniversities, research institutes, healthcare organizations

Neuroscience Research Engineers focus on applying engineering principles to develop neurotechnologies and solutions, often working in industry settings. Neuroscientists primarily conduct fundamental research to understand brain functions, typically in academic or clinical environments. Both roles require strong research skills but differ in their focus and application.

What are some common challenges faced by Neuroscience Research Engineers when integrating new technologies into ongoing research projects?

Neuroscience Research Engineers often encounter challenges when introducing cutting-edge tools or methodologies into established research workflows. Compatibility issues with existing equipment, data integration complexities, and the need for thorough validation to ensure data accuracy are frequent hurdles. Additionally, coordinating with interdisciplinary teams—including neuroscientists, software developers, and clinical staff—requires clear communication to align technical innovations with research objectives. Overcoming these challenges typically involves proactive planning, continuous learning, and strong collaborative skills.
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Neuroscience Research Engineer jobs near you
Infographic showing various Neuroscience Research Engineer job openings in the United States as of July 2026, with employment types broken down into 96% Full Time, 1% Part Time, and 3% Contract. Highlights an 87% Physical, 5% Hybrid, and 8% Remote job distribution, with an average salary of $106,012 per year, or $51 per hour.
AI Research Engineer

AI Research Engineer

The Path

San Francisco, CA • On-site

Full-time

Posted 2 days ago

Job description

Note - Below contains the outcomes and competencies for the team. If you bring standout strengths in some areas but not all, you are still encouraged to apply.
Mission
Design, train, ship, iterate on, and innovate on the AI brains behind The Path's AI Therapist. Combine research, data science, and engineering to create models, orchestration, and evaluation systems that make therapy conversations deeply effective, clinically grounded, and safe.
Outcomes
  1. Improve quality of AI Therapy: Deliver measurable improvements in conversation quality, therapeutic alliance, and user outcomes through fine-tuning strategies, training data curation, building RL environments, new model architectures and other AI innovations.
  2. Improve evaluation of AI quality: Improve on and maintain a robust eval stack that includes scripted tests, LLM-as-judge evaluations, human ratings, and safety checks. Improve automated regression testing, detection of defects, and observability (eg dashboards).
  3. Own AI system. Build, maintain, and iterate on the production codebase that delivers AI therapy and supports the evaluation and iteration of our AI.
  4. Productionize Models and Pipelines. Own The Path from notebook to production: training jobs, model packaging, deployment, monitoring, and rollback strategies. Keep latency, reliability, and cost within agreed budgets while enabling rapid iteration on new ideas.
  5. Improve Safety, Alignment, and Clinical Guardrails Work with clinicians and internal experts to encode clinical guidelines into prompts, reward functions, tools, and filters. Proactively identify and reduce harmful or low-quality behaviors through targeted experiments, red teaming, and mitigations.
  6. Own Research Roadmap and Experiment Velocity Run high-quality experiments from hypothesis to analysis to improve our understanding of what matters and what works. Shape and execute a focused R&D roadmap.
  7. Collaboration with Clinicians, Product, and Engineering. Translate product and clinical requirements into concrete model and system changes. Partner with full-stack product engineers so that new AI capabilities are easy to integrate and maintain in the product.
Competencies
  1. LLM and Applied ML Depth. Demonstrates strong experience with large language models, including fine-tuning, training data design, and model selection. Knows how to move core metrics on conversation quality and user outcomes, rather than chasing generic benchmarks. Can look at evals, transcripts, and metrics and quickly form grounded hypotheses for improvement.
  2. Ships clean, maintainable, quality code. No only do you know how transformers work, but you are also an engineer that has experience shipping production-level code and/or maintaining an AI system in production.
  3. Data Engineering Skills. Can set up production-level data pipelines for training new models, evals, analysis, etc.
  4. Scientific Mindset. You formulate hypotheses, and you are good at evaluating them (eg through experiments, data analysis, etc). You are consistently learning at the cutting edge, and you're able to leverage and communicate those learnings to make the entire company more successful.
  5. Ruthless Prioritizer. You are keenly aware of how to provide company value and to prioritize projects accordingly. Resistant to nerd-sniping.
  6. Quality Obsessive: Refuses to ship subpar work, continuously improving the codebase.
  7. Fast: Prioritizes speed by leveraging AI, breaking down complex tasks, shipping early, optimizing for learnings, iterating quickly, and avoiding over-engineering.
  8. Strong communicator. You can work collaboratively in a positive way. Sees others perspectives. Strong opinions, loosely held. Focused on user/business value, not ego.
Great to have
  • Personal or other experience with therapy or coaching
  • Domain knowledge of psychology, neuroscience, therapy, or coaching.

Apply