Computer Systems Engineer 3 – Optimization and AI for Scientific Discovery

Lawrence Berkeley National Laboratory • Berkeley, CA

15h • $156,864 - $191,724 • Onsite

About The Position

Berkeley Lab’s Applied Mathematics and Computational Research Division has an opening for a Computer Systems Engineer 3 - Optimization and AI for Scientific Discovery to develop and apply machine learning, agentic AI, optimization, and sampling tools to autonomous discovery. In this role, you will be part of the Applied Computing for Scientific Discovery (ACSD) Group, which focuses on enabling scientific discovery through advanced software applications, tools, and libraries across key Department of Energy (DOE) mission areas. You will play a key role within a multidisciplinary team combining elements of applied mathematics, optimization, statistics, machine learning, agentic artificial intelligence, and computational science to accelerate autonomous discovery and manufacturing scale-up. As part of this dynamic team, you will develop, test, and benchmark new optimization models, active learning and sampling strategies, and design novel machine learning and agentic frameworks that close the feedback loop for automated discovery.

Requirements

• Bachelor's degree in Applied Mathematics, Statistics, Machine Learning, Computational Science, or a related field with a minimum of 8 years of related experience; or a Master's degree with 6 years of related experience; or equivalent experience.
• Strong, demonstrated background in machine learning, artificial intelligence, numerical optimization, and programming.
• Demonstrated experience in the design, development, deployment, and application of machine learning, agentic AI, and multi-fidelity optimization algorithms.
• Proven experience developing software tools and algorithms for autonomous experimentation, inverse design, software optimization, or related domains, along with experience working on high-performance computing (HPC) platforms.
• Demonstrated analytical skills critical for designing, deploying, and applying AI/ML/optimization algorithms, paired with excellent Python programming skills.
• Experience with software project management and demonstrated experience leading cross-functional teams.
• Excellent oral, written, and interpersonal communication skills, with the ability and desire to work effectively within an energetic cross-disciplinary team.
• Proven ability to work effectively while balancing multiple competing priorities and tasks.

Nice To Haves

• Master's degree with a minimum of 6 years of experience, or a Ph.D. with 4 years of experience, or equivalent experience preferred.

Responsibilities

• Develop, apply, and deploy advanced software tools for numerical optimization, active learning, machine learning, and artificial intelligence tailored to science and engineering domains.
• Design, develop, test, benchmark, deploy, and tune agentic AI software frameworks and multi-fidelity optimization algorithms to close the feedback loop for automated scientific discovery.
• Deploy, optimize, and tune algorithms and software developments within high-performance computing (HPC) environments.
• Collaborate actively in a multidisciplinary team environment comprising scientists from energy technologies, physical sciences, mathematics, and computing.
• Resolve complex research and engineering issues by analyzing variable factors and exercising judgment to select optimal methods, techniques, and evaluation criteria.
• Publish developed algorithms as open-source software packages, maintain code documentation, and contribute to peer-reviewed journal articles and research proposals.
• Coordinate and lead software engineering and science teams in defining system requirements, software features, user interfaces, and overall software development processes.
• Mentor junior staff and developers while proactively establishing strategic partnerships with internal and external research teams to advance collaborative project goals.

Benefits

• Full-time, 5-year, term appointment with the possibility of conversion to Career appointment based upon satisfactory job performance, continuing availability of funds and ongoing operational needs.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior