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Internship Machine Learning Neuroscience Jobs (NOW HIRING)

Neuralink

Machine Learning Engineer

Austin, TX · On-site

$199K - $331K/yr

  • Medical

  • Dental

  • Vision

  • Retirement

No prior knowledge of neuroscience is required; we value simple solutions grounded in first ... Temporary Employees & Interns excluded

Neuralink

Machine Learning Engineer

Austin, TX · On-site

$199K - $331K/yr

  • Medical

  • Dental

  • Vision

  • Retirement

No prior knowledge of neuroscience is required; we value simple solutions grounded in first ... Temporary Employees & Interns excluded

Jane Street

Machine Learning Engineer

Manhattan, NY · On-site

Many classes and activities are shared with our Software Engineering interns, while others focus specifically on machine learning applications and techniques. Machine learning is a critical pillar of ...

Jane Street

Machine Learning Engineer

New York, NY

Many classes and activities are shared with our Software Engineering interns, while others focus specifically on machine learning applications and techniques. Machine learning is a critical pillar of ...

Jane Street

Machine Learning Engineer

New York, NY · On-site

Many classes and activities are shared with our Software Engineering interns, while others focus specifically on machine learning applications and techniques. Machine learning is a critical pillar of ...

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Internship Machine Learning Neuroscience information

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$25.5K

$42.6K

$88K

How much do internship machine learning neuroscience jobs pay per year?

As of Aug 17, 2026, the average yearly pay for internship machine learning neuroscience in the United States is $42,584.00, according to ZipRecruiter salary data. Most workers in this role earn between $32,500.00 and $46,000.00 per year, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as an internship machine learning neuroscience, and why are they important?

To thrive in an Internship Machine Learning Neuroscience role, you generally need a background in neuroscience, computer science, or a related field, along with a solid understanding of machine learning concepts. Experience with programming languages such as Python, libraries like TensorFlow or PyTorch, and familiarity with neuroimaging software are commonly required. Strong analytical thinking, problem-solving skills, and effective communication help you work collaboratively and adapt to complex research environments. These skills are essential for contributing meaningfully to interdisciplinary projects at the intersection of neuroscience and artificial intelligence.

What is the difference between Internship Machine Learning Neuroscience vs Internship Data Science?

AspectInternship Machine Learning NeuroscienceInternship Data Science
Required CredentialsBackground in neuroscience, machine learning, programmingBackground in statistics, programming, data analysis
Work EnvironmentResearch labs, healthcare, academia, tech companiesBusiness, tech firms, research institutions
Industry UsageNeuroscience research, AI development, healthcare techBusiness analytics, product development, consulting

Internship Machine Learning Neuroscience focuses on applying machine learning techniques to neuroscience data, often within research or healthcare settings. In contrast, Internship Data Science covers a broader range of data analysis across industries. Both roles require programming skills, but the focus and industry applications differ significantly.

What is an internship in machine learning neuroscience?

An Internship in Machine Learning Neuroscience is a temporary position, often for students or recent graduates, that involves applying machine learning techniques to neuroscience research. Interns may work on projects such as analyzing brain imaging data, modeling neural networks, or developing algorithms to understand brain function. These internships provide hands-on experience in both computational methods and neuroscience concepts, helping interns build valuable skills for future academic or industry roles. Opportunities can be found in universities, research institutes, or technology companies with neuroscience divisions.

What types of projects do interns typically work on in a machine learning neuroscience internship?

Interns in Machine Learning Neuroscience often engage in projects that combine data analysis, algorithm development, and neuroscience research. This can include tasks such as preprocessing neural data, building and evaluating machine learning models to interpret brain signals, or developing tools for data visualization. Interns frequently collaborate with both data scientists and neuroscientists, gaining hands-on experience with real-world datasets and exposure to interdisciplinary research environments. These projects help interns build practical skills and contribute meaningful insights to ongoing research.

What cities are hiring for Internship Machine Learning Neuroscience jobs?

Cities with the most Internship Machine Learning Neuroscience job openings:

What are the most commonly searched types of Machine Learning Neuroscience jobs?

The most popular types of Machine Learning Neuroscience jobs are:

What states have the most Internship Machine Learning Neuroscience jobs?

States with the most job openings for Internship Machine Learning Neuroscience jobs include:

Internship Machine Learning Neuroscience jobs near you

Internship - Machine Learning Engineer

Smule

Salt Lake City, UT • Remote

Other

Re-posted 15 days ago

Job description

Smule has been on a mission to bring the world together through music since 2008. Music is much more than listening… it's about creating, sharing, discovering, participating, and connecting with people. With dozens of millions of monthly active users creating over 20 million songs every day, Smule is connecting people all over the world through the joy of making music and transforming the music landscape from one of passive listening to collaborative creative expression and active engagement.


About the Role:

We are looking for a Machine Learning Engineer to own the end-to-end lifecycle of ML models in production at Smule, from training and optimization through deployment, monitoring, and iteration. You will work closely with research scientists to bring models off the bench and into scalable, reliable systems that serve millions of users. The ideal candidate is a strong engineer first, with deep practical knowledge of ML systems, a passion for reliability, and an eye for performance.


We strongly encourage candidates with non-traditional backgrounds to apply. If your path into ML engineering came through backend systems, DevOps, audio software, data engineering, or another field, we want to hear from you.


What You'll Be Doing:

  • Design, build, and maintain production ML pipelines encompassing data ingestion, feature engineering, model training, evaluation, and deployment.
  • Optimize models for production constraints including latency, throughput, memory footprint, and cost, using techniques such as quantization, distillation, pruning, and efficient serving architectures.
  • Implement robust monitoring, alerting, and observability for deployed models, covering data drift, prediction quality, and system health.
  • Collaborate with research scientists to integrate new model architectures and training techniques into production systems with minimal friction.
  • Build and improve CI/CD pipelines for ML, including automated testing, validation gates, and staged rollouts.
  • Manage compute infrastructure and costs, making informed tradeoffs between performance, reliability, and budget.


What We're Looking For:

  • Degree (B.S., M.S., or Ph.D.) in Computer Science, Software Engineering, Electrical Engineering, or a related technical discipline, or currently pursuing one.
  • Strong proficiency in Python and experience with deep learning serving (TorchServe, Triton, vLLM, or equivalent).
  • Solid understanding of systems engineering: networking, storage, containerization, orchestration, and monitoring.
  • Ability to reason about tradeoffs between latency, throughput, cost, and model quality.


Bonus Points For:

  • Experience serving large language models or other generative models at scale.
  • Familiarity with audio/music processing pipelines and real-time inference constraints.
  • Experience with Bayesian optimization, bandit algorithms, or adaptive experimentation platforms.
  • Contributions to open-source ML infrastructure projects.


Smule is an Equal Opportunity Employer and considers all qualified applicants without regard to race, color, religion, sex, gender identity or expression, sexual orientation, national origin, ancestry, age, disability, medical condition, genetic information, marital status, military or veteran status, or any other protected characteristic under federal, state, or local law.


We are committed to creating an inclusive environment for all employees and applicants. If you require a reasonable accommodation during the application or interview process, please let us know.