Machine Learning Engineer - WiMLDS Inc

Machine Learning Engineer – 102701
Division: NE-NERSC

Lawrence Berkeley National Lab’s (LBNL) NERSC Division has an opening for a Machine Learning Engineer to join the team.

In this exciting role, you will apply wide-ranging expertise to support science and advanced analytics.

You will be a part of multidisciplinary and cross-institution projects, involving academic and industry partners both in domain sciences as well as in machine-learning. Responsibilities include supporting the ML/DL software stack on NERSC supercomputers, deploying new cutting-edge tools and frameworks for scalable ML/DL workflows, and working with scientists to apply ML/DL techniques to their research.

The selected candidate(s) will be hired at a Level 3 or Level 4 depending on their level skills and experience.

What You Will Do At Level 3:

• Support the ML/DL software stack on NERSC supercomputers, deploy new cutting-edge tools and frameworks for scalable ML/DL workflows.
• Collaborate with scientists and industry partners to develop new applications of machine learning opening the door to new science.
• Provide expert ML/DL advice, consultancy services, and training events to scientists and users of NERSC computing resources.
• Engage with the ML academic communities to stay on top of the latest advancements in ML.
• Shape future NERSC supercomputers, evaluating new hardware architectures for AI.
• Determine methods and procedures on new assignments and may coordinate activities of other personnel.
• Network with key contacts outside your area of expertise.
• Work on and resolve complex issues where analysis of situations or data requires an in-depth evaluation of variable factors.
• Exercise judgment in selecting methods, techniques and evaluation criteria for obtaining results.

In Addition, The Level 4 Will:

• Mentor early career staff members in AI/ML techniques and projects.
• Stay abreast of new and emerging AI/ML trends, through R&D collaborations, literature, workshops and conferences; translate these new directions into actionable opportunities for NERSC or NERSC users.
• Develop strategy for addressing both performance, as well as productivity requirements of the NERSC AI/ML for science community.
• Work on and resolve significant and unique issues where analysis of situations or data requires an evaluation of intangibles.
• Exercise independent judgment in methods, techniques and evaluation criteria for obtaining results.

What Is Required At Level 3:

• Bachelor’s degree in Physical Sciences, Computer Science or related field or equivalent is required. Masters and PhD degrees in similar disciplines are preferred.
• Typically requires a minimum of 8 years of related experience with a Bachelor’s degree; or 6 years and a Master’s degree; or equivalent experience.
• Wide-ranging experience in the areas of machine learning and data science, as applied to scientific data.
• Ability to troubleshoot and resolve complex issues in creative and effective ways.
• Ability to network and collaborate with key contacts outside your area of expertise.
• Excellent oral and written communication skills.
• Proven ability to work productively both independently and as part of an interdisciplinary team balancing divergent objectives involving research, code development, supporting software and consulting with scientists.

In Addition, The Level 4 Requires:

• Typically requires a minimum of 12 years of related experience with a Bachelor’s degree; or 8 years and a Master’s degree; or equivalent experience.
• Broad expertise and/or unique knowledge in the areas of AI/ML technology is required.
• Ability to work on and resolve significant and unique issues where analysis of situations or data requires an evaluation of intangibles.
• Ability to exercise independent judgment in methods, techniques and evaluation criteria for obtaining results.

Desired Qualifications:

• Familiarity with multiple deep learning architectures and technologies.
• A proven track record of publications in Deep Learning at machine learning or domain science venues.
• Familiarity with computing hardware, GPUs and/or AI accelerators.
• Familiarity with performance profiling, benchmarking, optimization and scaling of Deep Learning architectures on HPC systems.

Open until filled, but for full consideration, apply by Oct 31.

Notes:

• This is a full-time, career appointment, exempt (monthly paid) from overtime pay.
• 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 requires substantial on-site presence, but is eligible for a flexible work mode, and hybrid schedules may be considered. Hybrid work is a combination of performing work on-site at Lawrence Berkeley National Lab, 1 Cyclotron Road, Berkeley, CA and some telework. Individuals working a hybrid schedule must reside within 150 miles of Berkeley Lab. Work schedules are dependent on business needs. In rare cases, full-time telework or remote work modes may be considered.

Want to learn more about working at Berkeley Lab? Please visit: careers.lbl.gov

How To Apply
Apply directly online and follow the on-line instructions to complete the application process.

Berkeley Lab is committed to inclusion, diversity, equity and accessibility 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.