Machine Learning Engineer

$126,900 - $214,140/Yr

Lawrence Berkeley National Laboratory - Berkeley, CA

Full-time - Mid Level

Berkeley, CA

Ambulatory Health Care Services

About the position

The Machine Learning Engineer position at Lawrence Berkeley National Lab's NERSC Division involves applying expertise in machine learning and data science to support advanced analytics and scientific research. The role includes collaborating with multidisciplinary teams, deploying ML/DL software on supercomputers, and mentoring early career staff. The position is available at Level 3 or Level 4, depending on the candidate's experience and skills.

Responsibilities

• 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.
• 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 updated on the latest advancements in ML.
• Shape future NERSC supercomputers by evaluating new hardware architectures for AI.
• Determine methods and procedures on new assignments and coordinate activities of other personnel.
• Network with key contacts outside your area of expertise.
• Work on and resolve complex issues requiring in-depth evaluation of variable factors.

Requirements

• Bachelor's degree in Physical Sciences, Computer Science or related field or equivalent is required.
• 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 machine learning and data science as applied to scientific data.
• Ability to troubleshoot and resolve complex issues creatively and effectively.
• Excellent oral and written communication skills.
• Proven ability to work productively both independently and as part of an interdisciplinary team.

Nice-to-haves

• 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.

Benefits

• Flexible work mode with hybrid schedules considered.
• Full-time career appointment with competitive salary ranges.