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Trainee Machine Learning Neuroscience Jobs (NOW HIRING)

Biogen

Principal Machine Learning Engineer

As a Principal Machine Learning Engineer focused on Clinical Trial Forecasting and Optimization ... in neuroscience, neurology, immunology, and rare diseases. Your contributions will directly ...

Qualcomm

Machine Learning R&D

San Diego, CA · On-site

Engineering Group, Engineering Group > Machine Learning Researcher General Summary: About the Team ... Physics, Neuroscience, Economics, and VLSI Design, united by our passion to solve challenging ...

Qualcomm

Machine Learning R&D

San Diego, CA

Engineering Group, Engineering Group > Machine Learning Researcher General Summary: About the Team ... Physics, Neuroscience, Economics, and VLSI Design, united by our passion to solve challenging ...

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

As of Jun 1, 2026, the average yearly pay for trainee machine learning neuroscience in the United States is $43,530.00, according to ZipRecruiter salary data. Most workers in this role earn between $33,000.00 and $51,000.00 per year, depending on experience, location, and employer.
What cities are hiring for Trainee Machine Learning Neuroscience jobs? Cities with the most Trainee 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 Trainee Machine Learning Neuroscience jobs? States with the most job openings for Trainee Machine Learning Neuroscience jobs include:
Trainee Machine Learning Neuroscience jobs near you
Infographic showing various Trainee Machine Learning Neuroscience job openings in the United States as of May 2026, with employment types broken down into 99% Full Time, and 1% Contract. Highlights an 30% Physical, 2% Hybrid, and 68% Remote job distribution, with an average salary of $43,530 per year, or $20.9 per hour.

Machine Learning Researcher - Springtail

Astera

Emeryville, CA

Other

Posted 3 days ago

Job description

About Astera
Astera is a private foundation on a mission to steer science and technology toward an abundant future. We believe the coming years will bring an era of unprecedented scientific and technological advancement as exponential progress in AI converges with central advances in other fields to dramatically accelerate innovation. This inflection point provides an unparalleled opportunity to fundamentally rethink the institutions, systems, and tools that drive scientific progress.
Unlike traditional non-profit research organizations, Astera funds and operates projects like high-velocity startups, allowing us to focus on ambitious goals, match structure to problem, and attract strong technical talent and leadership. You can read more about our mission, vision, and programming here.
Position Summary
Datasets in many areas, science in particular, are often small, heterogeneous, and expensive. Human scientists can take these datasets and generate models to describe them, but this process of model induction is labor-intensive and error-prone. Machine learning is a general and scalable solution, but it is not uniformly sample efficient.
The Astera Institute is seeking a Machine Learning Researcher to help surmount this barrier with new architectures for data-efficient and general model induction. This includes bootstrapped program synthesis, along with components for a system that synthesizes its own learning algorithms - a machine learning strange loop. This is a full time position that reports to Timothy Hanson.
Responsibilities
  • Hypothesize, test, and refine means of improving generalization performance of common architectural elements, including different forms of attention. This includes devising controlled datasets to elucidate e.g. learning order & learned representations.
  • Think both mathematically and empirically about problems of runtime inference in gradient-trained networks, with an eye to the extensive literature on statistical learning and an open mind to the many forms of constrained optimization.
  • Contribute to a well-documented and well-instrumented code base that is performant where necessary yet expeditious where experimental throughput demands.
Qualifications and Experience
  • Masters or equivalent in machine learning, mathematics, or equivalent fields (strong candidates from neuroscience are encouraged to apply).
  • Fluency with Pytorch, and familiarity with JAX, CUDA, and/or Triton + their open-source ecosystems.
  • Demonstrated ability do fundamental research.
  • Demonstrated ability to work in teams.
Location
This position is hybrid at our office in Emeryville, CA. Some travel may be required from time-to-time for in-person collaboration and work.
Compensation
The posted salary range is based on location in the Bay Area. The successful candidate will receive a competitive compensation package, commensurate with their experience and location.
  • Benefits summary

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