Applied Machine Learning - Lead

Key Skills

Applied Machine Learning, C Programming, C++, Caffe, Deep Learning, Java Programming, Keras software library, Machine learning techniques, Python Programming, PyTorch, Scala Programming, TensorFlow

Job Description

Join us and make YOUR mark on the World!

Are you interested in joining some of the brightest talent in the world to strengthen the United States’ security? Come join Lawrence Livermore National Laboratory (LLNL) where our employees apply their expertise to create solutions for BIG ideas that make our world a better place.

We are committed to a diverse and equitable workforce with an inclusive culture that values and celebrates the diversity of our people, talents, ideas, experiences, and perspectives. This is essential to innovation and creativity for continued success of the Laboratory’s mission.

Pay Range

• $148,650.00 - $190,932.00 Annually for the SES.3 level
• $178,410.00 - $229,116.00 Annually for the SES.4 level

Please note that the pay range information is a general guideline only. Many factors are taken into consideration when setting starting pay including education, experience, the external labor market, and internal equity.

Job Description

We are seeking highly qualified scientists and engineers to join our interdisciplinary team of Applied Machine Learning Researchers and Analysts. You will work with researchers from a variety of fields to provide solutions, leading projects supporting a variety of application areas such as Cyber Security, Climate Modelling, Energy Systems, Bioscience and Advanced Manufacturing. Qualified candidates should have in-depth knowledge of machine learning toolkits and a background in applied research in machine learning or a complementary scientific discipline providing underlying skills in data analytic techniques. While supporting applied research projects, selected candidates will provide mentorship and practical training to junior members of the group to help them develop depth and breadth in machine learning techniques. These positions are in the Computational Engineering Division (CED) within the Engineering Directorate.

This position will be filled at either level based on knowledge and related experience as assessed by the hiring team. Additional job responsibilities (outlined below) will be assigned if hired at the higher level. 

In this role you will

• Lead research efforts in Machine Learning to enable development of new state-of-the-art algorithms for Laboratory problem domains and bring research results to practical use in LLNL national security programs.
• Mentor and advise LLNL scientists and engineers in data science best practices.
• Coordinate data collection, processing, and labeling efforts, including but not limited to understanding the data through the use of visualization and statistical methods, cleaning/organizing the data, and representing the data in a format suitable for Machine Learning algorithms.
• Develop implementation, training and validation plans for proposed new state-of-the-art Machine Learning algorithms for Laboratory problem domains.
• Serve as a primary point of contact with inter-organizational contacts and/or external customers.
• Adapt general methodology to practice meeting project-specific requirements.
• Perform other duties as assigned.

Additional job responsibilities, at the SES.4 level 

• Determine project scope, schedule and budget and lead a team of multidisciplinary personnel who conduct analysis under your direct supervision.
• Propose and execute development plans meeting medium/long term objectives for Laboratory problem domains.

Qualifications

• Ability to secure and maintain a U.S. DOE Q-level security clearance which requires U.S. citizenship.
• PhD in Computer Science, Computational Engineering, Applied Statistics, Applied Mathematics or the equivalent combination of education and related experience.
• Expertise developing and applying algorithms in one or more of the following Machine Learning areas/tasks: deep learning, unsupervised/self-supervised learning, representation learning, zero- or few-shot learning, active learning, reinforcement learning, natural language processing, ensemble methods, statistical modeling and inference (e.g., probabilistic graphical models, Gaussian processes, or nonparametric Bayesian methods).
• Experience in the broad application of one or more higher-level programming languages such as Python, Java, Scala, or C/C++.
• Experience specifying data requirements and quality control for Machine Learning applications.
• Experience with one or more deep learning libraries such as PyTorch, TensorFlow, Keras, or Caffe.
• Ability to work independently under general direction within the scope of an assignment and use sound judgment in determining methods, techniques, and evaluation criteria.
• Advanced verbal and written communication skills necessary to effectively collaborate in a team environment, present technical ideas/results and provide advice to management.
• Ability to travel off-site for sponsor and customer interaction.

Additional qualifications at the SES.4 level

• Significant experience managing projects and teams.
• Significant experience effectively managing concurrent technical tasks with competing priorities and implementing advanced research concepts in a multi-disciplinary team environment where commitments and deadlines are important to project success.
• Significant experience collaborating across research (university or national laboratory) and commercial (industry) institutions.