Computer Systems Engineer in Supercomputing performance
1 Cyclotron Road, Berkeley, CA
Applications have closed
Lawrence Berkeley National Lab
Computer Systems Engineer in Supercomputing performance
Organization: CR-Computational Research
Berkeley Lab’s Computational Research Division has an immediate opening for a Computer Systems Engineer to apply performance analysis and modeling techniques for HPC and AI applications in distributed memory supercomputers and engage in application tuning.
What You Will Do:
• Performance Analysis, Modeling, and Tuning of HPC and AI workloads.
• Build productive internal/external working relationships.
Additional responsibilities if hired at a higher level:
• More experienced Computer Systems Engineers will participate in a multidisciplinary team involving computer scientists, applied mathematicians, and domain scientists with the purpose of developing and deploying these codes for the solution of scientific and engineering problems
• Network with senior internal and external personnel in their own area of expertise.
What is Required:
• Typically requires a Bachelor’s degree in computer science, applied math, computational science, or a related technical field as well as a minimum of 2 years of related experience; or an advanced degree without experience; or equivalent work experience.
• Programming Proficiency in C/C++, Python, and the binding between these programming languages.
• Experience in runtime, or algorithm development.
• Excellent written and oral communication skills.
Additional Desired Qualifications:
• Bachelor’s degree and 5 years of related experience or equivalent combination of education and experience
• Familiarity with accelerator programming models (open-standard offload models, e.g., OpenMP, • OpenACC, or vendor-specific models, such as CUDA).
• Familiarity with AI frameworks pytorch, tensorflow, etc.
• Familiarity with performance tools.
• Familiarity with computer architectures and performance bottlenecks.
• Demonstrated ability to work effectively as part of a cross-disciplinary team.
Notes:
• This is a full-time, 2-year, term appointment with the possibility of extension or conversion to Career appointment based upon satisfactory job performance, continuing availability of funds, and ongoing operational needs.
• This position will be hired at a level commensurate with the business needs and the skills, knowledge, and abilities of the successful candidate.
• This position may be subject to a background check. Any convictions will be evaluated to determine if they directly relate to the responsibilities and requirements of the position. Having a conviction history will not automatically disqualify an applicant from being considered for employment.
• This position has the option of working remotely but limited to individuals residing in the United States.
• This position will be remote initially but limited to individuals residing in the United States tentatively until 2021 due to COVID-19. Once the Bay Area shelter-in-place restrictions are lifted, work will be primarily performed at Lawrence Berkeley National Lab, 1 Cyclotron Road, Berkeley, CA.
How To Apply
Apply directly online at http://50.73.55.13/counter.php?id=209331 and follow the on-line instructions to complete the application process.
Berkeley Lab is committed to Inclusion, Diversity, Equity and Accountability (IDEA) and strives to continue building community with these shared values and commitments.
Berkeley Lab is an Equal Opportunity and Affirmative Action Employer. We heartily welcome applications from women, minorities, veterans, and all who would contribute to the Lab’s mission of leading scientific discovery, inclusion, and professionalism. In support of our diverse global community, all qualified applicants will be considered for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, age, or protected veteran status.
Tags: C++ Computer Science CUDA Engineering HPC PHP Python PyTorch Research TensorFlow
Perks/benefits: Career development
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