Machine Learning Scientist

Salary depends on experience
Posted on 02/23/18
Less than 5 years experience
Business Services
Salary depends on experience
Posted on 02/23/18

Job Overview:

The machine learning scientist will apply machine learning (ML) to the earth system sciences as part of a newly-formed Analytics and Integrative Machine Learning (AIML) Group in the Technology Development Division (TDD) in the Computational and Information Systems Laboratory (CISL) at the National Center for Atmospheric Research (NCAR). The incumbent will have a proven ability to apply ML algorithmic approaches to problems in earth systems science or related physical science disciplines. Such application experience may include use of ML training and inference systems for the recognition, prediction or tracking of important features or events in datasets, or alternatively, through the auto-encoding of suitable physics parameterizations in earth system models with neural networks, the replacement of model components with efficient, learned emulators. Machine learning could also be applied by the scientist to automate the human analysis of hundreds of routine data products, thus amplifying the scientific capability of researchers, the integration of non-traditional data sources into earth system prediction systems, to help optimize supercomputing workflows through ML-guided resource management, or for the early detection and steering of numerical simulations.


The machine learning scientist’s efforts will be built on top of NCAR’s core capability in domain-focused statistical development, and will leverage its vast observational and model output datasets, and CISL’s petascale supercomputing infrastructure. The position will require the ability to work in teams and across disciplines in order to cross-fertilize ideas and build strong collaborations to tackle earth system science challenges. This integration with and support from colleagues in the earth system sciences will help to ensure the relevance and sustainability of the ML scientist’s research activities.



Main Duties

  • The position collaborates with scientists in CISL, the Research Applications Laboratory (RAL), the Climate and Global Dynamics Laboratory (CGD), and the High Altitude Observatory (HAO) at NCAR, and potentially with external data scientists as well, to apply machine learning techniques to challenging earth system science problems.
  • The initial focus will be on three auto-encoding problems relevant to NCAR’s mission: modeling atmospheric turbulence, emulating cloud microphysics, and predicting space-weather impacts from interplanetary coronal mass ejections (ICMEs). These three distinct modeling problems will provide NCAR an experimental and methodological framework for training, testing, evaluating and deploying ML-based auto-encoders within its modeling enterprise.
  • The position provides high level machine learning expertise to this project, assists in planning the projects human and financial resource requirements, and will participate in the evaluation of the project’s progress, its results, and make adjustments to the project’s approach to better achieve objectives.
  • The machine learning scientist may also serve, from time to time, as a consultant to internal staff and external organizations on machine learning topics.


Communication of Results

  • Participates in mission-relevant academic activities including conferences, workshops and tutorials. Documents research results by authoring peer-reviewed conference and journal publications, and publicizes those results in presentations at scientific meetings.
  • As a subject matter expert, helps develop grant proposal concepts, teams and text, and may be called upon to serve as a principal or co-principal investigator.


Community Service

  • Serves as a reviewer on scientific papers and proposals, or on conference organizing committees. Supports outreach activities and consults to increase awareness and knowledge of machine learning as a tool in earth system science.



  • Ph.D. in Computer Science or in a physical science discipline which uses machine learning and at least two years of post-graduate experience in the scientific field of specialization; or an equivalent combination of education and experience.
  • Ability to work both independently or collaboratively as a group lead to solve routine and/or occasionally complex technical problems. Ability to organize, prioritize and coordinate multiple tasks. Ability to conduct research with minimal supervision. 
  • Experience mentoring and working with students.  May supervise the work of others, including project staff.  
  • Expert knowledge of multi-/many-core computer architectures, compilers, and supporting libraries. Awareness of recent developments in the area of computer architecture and high-performance computing. Advanced knowledge of performance programming paradigms for high performance systems, such as MPI, OpenMP, and OpenACC. Experience using high performance computing environments. 
  • Ability to understand and modify code written in Fortran90, C and Python. Ability to work with development platforms such as Unity, Visual Studio, Nsight, OSVR, etc. 
  • Excellent communication skills in presenting scientific research, and writing papers in scientific journals, technical reports and proposals. Ability to work and communicate with an international and multi-disciplinary team.
  • Advanced knowledge of one or more machine learning algorithms and the supporting mathematics. 
  • Knowledge of one deep learning framework, (e.g. Theano, TensorFlow, Keras), Torch, and familiarity with problem solving environments like Jupyter Notebooks. Knowledge of at least one general machine learning framework (e.g. scikit-learn). 
  • Able to use statistical methods to evaluate machine learning models. 
  • Experience in at least one high-level language (e.g. Python, R, or Matlab) and at least one low-level language (e.g. C, C++ or Java) 
  • Expertise in Linux, and Unix tools: User level familiarity with Linux and Unix-based tools for scripting and file manipulation.



  • Experience in computational earth system science and/or in atmospheric science is desirable.
  • Demonstrated record of publications Familiarity with distributed/shared memory parallel computing a plus.
  • Familiarity with the use of commercial cloud services will be helpful.
  • Familiarity with geoscience applications
  • Knowledge of CUDA or OpenCL


What’s In It for You:

  • Compensation
  • PTO
  • Paid Holidays
  • Tuition Reimbursement
  • Benefits (Medical, Dental, Vision)
  • Retirement Plan
  • And more!
  • 18087

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