Machine Learning Intern (Remote)

Preferable Location(s): Boston, United States of America
Work Type: Internship

Julia Computing is looking for interns in machine learning engineering to integrate scientific machine learning (SciML) into the JuliaSim, JuliaSPICE, and Pumas cloud-based platforms. 

Julia Computing's mission is to develop products that bring Julia's superpowers to its customers. Julia Computing's flagship product is JuliaHub, a secure, cloud based, software-as-a-service platform for developing Julia programs, deploying them, and scaling to thousands of nodes. Julia Computing was founded in 2015 by the creators of the Julia programming language for artificial intelligence, machine learning, analytics, data science, modeling, and simulation.

Applicants should include a cover letter describing their previous experience in building machine learning tooling (not using machine learning tooling), experience programming in Julia, and describe the timeframe on which they are looking for an internship (one semester in fall/spring/summer, part time vs full time, etc.). Any application without a cover letter will be immediately rejected.

Job Duties and Responsibilities

  • Develop MLOps platforms for automating the hyperparameter optimization of neural network training
  • Integrate the latest techniques of scientific machine learning into the JuliaSim cloud-based platform
  • Create pretrained surrogates of physical models using physics-informed neural networks (PINNs)
  • Investigate new methods for neural-surrogates of partial differential equations like Fourier Neural Operators
  • Develop and demonstrate transfer learning tooling for accelerated physics-informed neural network training

Education, Expertise and Experience Required

  • A bachelor’s or master’s degree or equivalent in mathematics, computer science, engineering, or related technical disciplines. In progress degrees are acceptable for internships.
  • Strong knowledge of at least one dynamic programming language (of course Julia is preferred) or machine learning framework (e.g. TensorFlow, PyTorch, or JAX. Flux.jl is preferred)
  • Demonstrated capabilities through prior work in at least one of: code optimization, differential equations, package development, or machine learning

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