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86111 Requisition #
Berkeley Lab’s Computational Science Department has immediate openings for multiple Computer Systems Engineers to assist in the implementation and documentation of new high-performance computing (HPC) approaches to a variety machine learning challenges in the Physics Sciences.  
 

Over the course of the last five years, Berkeley Lab’s Computational Research Division in collaboration with NERSC Data Analytics group and the Physics Division has developed a research program in data-driven pattern recognition algorithms for High Energy Physics (HEP) and Cosmology, targeting massively parallel and post-Moore architectures (including neuromorphic and quantum systems). Several promising research directions involve the development of distributed Geometric Deep Learning, algorithms based on distributed graph neural networks, as well as Generative Adversarial Networks.

 
The selected candidates will be hired at the CSE 1, 2, or 3 level.  Classification will depend upon the candidate's level of skills, knowledge, and abilities.

The Computer Systems Engineer will:
  • In the context of the DOE ExaLearn Co-Design Center, collaborate with LBNL physicists and computer scientists to support development of innovative distributed pattern recognition algorithms for the next generation of HEP and Cosmology experiments and simulations on HPC systems.
  • Develop workflows for distributed training and optimization of neural networks, GANs and regression algorithms as we push to Exascale.
  • Profile and benchmark deep learning codes to identify data management, I/O, and overall workflow bottlenecks.
  • Implement and tune software solutions to address bottlenecks in deep learning frameworks, I/O middleware, and file system parameters on current HPC systems.
  • Run performance tests on multiple DOE HPC systems, such as Summit @ ORNL and Cori @ LBNL, to characterize the effectiveness of solutions created.
  • Work on and troubleshoot problems of moderate scope where analysis of situations or data requires a review of a variety of factors.
  • Contribute to one or more existing research projects dedicated the development of Machine Learning algorithms for Cosmology and HEP.
 
In addition to the duties outlined above
 
At the CSE2 level
  • Apply professional experience and work at higher level of independence when completing assignments.
  • Work on and troubleshoot problems of diverse scope where analysis of data requires evaluation of identifiable factors.

At the CSE3 level
  • Apply wide-ranging experience and expertise to determine methods and procedures on new assignments and may coordinate activities of other personnel.
  • Work on and troubleshoot complex issues where analysis of situations or data requires an in-depth evaluation of variable factors.
 
Marginal
  • Contribute to one or more existing research projects dedicated the development of Machine Learning algorithms for Cosmology and HEP.

 
Minimum Qualifications:
  • Requires a Bachelor’s degree and 2 years related experience (or higher degree) or equivalent experience.
  • Background in I/O middleware, including familiarity with multiple storage architectures and technologies.
  • Knowledge of and some basic experience with GPGPU programming.
  • Familiarity with performance profiling, benchmarking, optimizing, and/or scaling applications on HPC systems.
  • Strong programming ability – preferably in Python, C, and/or C++
  • Strong oral and written communication skills.
  • Demonstrated ability to work effectively as part of a cross-disciplinary team.
 
In addition to the qualifications outlined above
 
At the CSE2 level
  • Requires a Bachelor’s degree and 5 years related experience (or higher degree) or equivalent experience.
  • Experience in I/O middleware, including familiarity with multiple storage architectures and technologies.
  • Demonstrated experience with GPGPU programming.
  • Knowledge and experience with performance profiling, benchmarking, optimizing, and/or scaling applications on HPC systems.
  • Ability to troubleshoot diverse problems and resolve a wide range of issues in creative ways.
 
at the CSE3 level
  • Requires a Bachelor’s degree and 8 years related experience (or higher degree) or equivalent experience
  • Demonstrated experience in I/O middleware, including familiarity with multiple storage architectures and technologies.
  • Demonstrated expertise with GPGPU programming.
  • Demonstrated skill and expertise with performance profiling, benchmarking, optimizing, and/or scaling applications on HPC systems.
  • Ability to apply professional concepts and company objectives to resolve complex issues in creative and effective ways.
Additional Desired Qualifications:
  • A PhD in computer science, physics, or related fields.
  • Experience, knowledge and / or significant interest in applying I/O middleware optimizations to scientific data.
  • Familiarity with I/O middleware packages, file systems, and/or storage architectures (e.g. HDF5, netCDF, Lustre, GPFS, etc)
  • Experience, knowledge, and/or significant interest in applying multiple machine learning techniques to scientific data
  • Experience or interest in HEP computing, cosmology data analysis, cosmological simulation
  • Background and experience in computational methods and scientific computing.
  • Experience in software performance evaluation and optimization
  • Knowledge of HPC systems
The posting shall remain open until the position is filled, however for full consideration, please apply by close of business on September 29, 2019.

Notes:
  • This is a full time career appointment.
  • Classification will depend upon the applicant's level of skills, knowledge, and abilities.
  • Full-time, M-F, exempt from overtime pay (monthly paid).
  • Salary is commensurate with experience.
  • This position is contingent on the successful completion of a background check.
  • Work will be primarily performed at Lawrence Berkeley National Lab, 1 Cyclotron Road, Berkeley, CA.


Berkeley Lab (LBNL) addresses the world’s most urgent scientific challenges by advancing sustainable energy, protecting human health, creating new materials, and revealing the origin and fate of the universe. Founded in 1931, Berkeley Lab’s scientific expertise has been recognized with 13 Nobel prizes. The University of California manages Berkeley Lab for the U.S. Department of Energy’s Office of Science.
 
Equal Employment Opportunity: Berkeley Lab is an Equal Opportunity/Affirmative Action Employer. All qualified. applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, age, or protected veteran status. Berkeley Lab is in compliance with the Pay Transparency Nondiscrimination Provision under 41 CFR 60-1.4.  Click here to view the poster and supplement: "Equal Employment Opportunity is the Law."

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Equal Employment Opportunity: Berkeley Lab is an Equal Opportunity/Affirmative Action Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, age, or protected veteran status. Berkeley Lab is in compliance with the Pay Transparency Nondiscrimination Provision under 41 CFR 60-1.4. Click here to view the poster and supplement: "Equal Employment Opportunity is the Law."

 

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The Lawrence Berkeley National Laboratory provides accommodation to otherwise qualified internal and external applicants who are disabled or become disabled and need assistance with the application process. Internal and external applicants that need such assistance may contact the Lawrence Berkeley National Laboratory to request accommodation by telephone at 510-486-7635, by email to eeoaa@lbl.gov or by U.S. mail at EEO/AA Office, One Cyclotron Road, MS90R-2121, Berkeley, CA 94720. These methods of contact have been put in place ONLY to be used by those internal and external applicants requesting accommodation.