Join us and make YOUR mark on the World!
Come join Lawrence Livermore National Laboratory (LLNL) where we apply science and technology to make the world a safer place; now one of 2019 Best Places to Work by Glassdoor!
We are seeking motivated and talented entry-level Machine Learning Researchers to assist in conducting basic and applied research in Machine Learning for automated understanding of massive multimodal data. At Lawrence Livermore National Laboratory (LLNL), we are developing new Machine Learning technologies, enabled by our world-class supercomputing facilities, to train and analyze the largest datasets in support of our national security and national science applications. These positions are in the Computational Engineering Division (CED), within the Engineering Directorate.
These positions will be filled at either the SES.1 or SES.2 level depending on your qualifications. Additional job responsibilities (outlined below) will be assigned if you are selected at the higher level.
- Conduct data processing efforts, including but not limited to understanding the data through the use of visualization and statistics methods, cleaning/organizing the data, and applying state-of-the-art deep learning models to embed the data for input into Machine Learning algorithms.
- Conduct paper/code surveys of state-of-the-art Machine Learning algorithms relevant to the problem being addressed.
- Contribute to research efforts in Machine Learning to enable development of new state-of-the-art algorithms for Laboratory problem domains.
- Conduct implementation, training and validation of proposed new state-of-the-art algorithms for Laboratory problem domains.
- Conduct development of interactive web apps that serve to demonstrate the newly developed algorithms.
- Contribute to the integration of algorithms within larger programmatic systems that require these capabilities.
- Participate in interactions with inter-organizational contacts and/or external customers.
- Assist in representing the organization by providing input on technical issues for specific projects including preparing and presenting technical reports.
- Perform other duties as assigned.
In Addition at the SES.2 Level
- Research, develop, and apply solutions to moderately complex Machine Learning problems of programmatic interest.
- Publish papers in peer-reviewed journals and present results at scientific meetings and conferences.
- Contribute to proposals.
- Bachelor's degree in Computer Science, Computational Engineering, Applied Statistics, Applied Mathematics or the equivalent combination of education and related experience.
- Fundamental knowledge of and/or experience developing and applying algorithms in one or more of the following Machine Learning areas/tasks: deep learning, unsupervised feature learning, zero- or few-shot learning, active learning, reinforcement learning, multimodal learning, natural language processing, ensemble methods, scalable online estimation, and probabilistic graphical models.
- Experience in the broad application of one or more higher-level programming languages such as Python, Java/Scala, Matlab, R or C/C++.
- Experience with one or more deep learning libraries such as TensorFlow, PyTorch, Keras, Caffe or Theano.
- Experience with interactive web app development such as Flask or Bokeh.
- Ability to work independently under general direction within the scope of an assignment and use sound judgment in determining methods, techniques, and evaluation criteria.
- Sufficient verbal and written communication skills necessary to effectively collaborate in a team environment and present technical ideas/results.
In Addition at the SES.2 Level
- Comprehensive knowledge and experience with Machine Learning algorithm development, with deep learning model development using TensorFlow, PyTorch, Keras, Caffe or Theano, and with interactive web app development such as Flask or Bokeh.
- Experience successfully developing code that is well written, designed and documented.
- Proficient verbal and written communication skills to collaborate in a team environment, publish and present technical ideas at top-tier Machine Learning workshops or conferences, and inform management.
- Master's degree in Computer Science, Computational Engineering, Applied Statistics, Applied Mathematics or the equivalent combination of education and related experience.
Pre-Employment Drug Test: External applicant(s) selected for this position will be required to pass a post-offer, pre-employment drug test. This includes testing for use of marijuana as Federal Law applies to us as a Federal Contractor.
Security Clearance: This position requires a Department of Energy (DOE) Q-level clearance.
If you are selected, we will initiate a Federal background investigation to determine if you meet eligibility requirements for access to classified information or matter. In addition, all L or Q cleared employees are subject to random drug testing. Q-level clearance requires U.S. citizenship. If you hold multiple citizenships (U.S. and another country), you may be required to renounce your non-U.S. citizenship before a DOE L or Q clearance will be processed/granted.
Note: This listing has two openings; these are Career Indefinite positions. Lab employees and external candidates may be considered for these positions.
Lawrence Livermore National Laboratory (LLNL), located in the San Francisco Bay Area (East Bay), is a premier applied science laboratory that is part of the National Nuclear Security Administration (NNSA) within the Department of Energy (DOE). LLNL's mission is strengthening national security by developing and applying cutting-edge science, technology, and engineering that respond with vision, quality, integrity, and technical excellence to scientific issues of national importance. The Laboratory has a current annual budget of about $2.1 billion, employing approximately 6,800 employees.
LLNL is an affirmative action/ equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, marital status, national origin, ancestry, sex, sexual orientation, gender identity, disability, medical condition, protected veteran status, age, citizenship, or any other characteristic protected by law.
Apply on company website