Machine Learning (ML) Bioengineer

Job description

Company Description Join us and make YOUR mark on the World! Lawrence Livermore National Laboratory (LLNL) has turned bold ideas into world-changing impact advancing science and technology to strengthen U.S. security and promote global stability. Our mission spans four critical national security areas nuclear deterrence, threat preparedness, energy security, and multi-domain defense empowering teams to take on the toughest challenges of today and tomorrow. With a culture built on innovation and operational excellence, LLNL is a place where your expertise can make a real impact. Job Description We have an opening for a Machine Learning (ML) Bioengineer to conduct research training and evaluating next-generation clinical, protein and genome language models. You will join the Bioresilience Incubator, a dynamic engineering center that integrates predictive computational modeling, machine learning, and experimental biology to advance national security and public health missions. This position will be in the Computational Engineering Division (CED), within the Engineering Directorate, matrixed to the Bioresilience Incubator. As a member of our multidisciplinary team, you will collaborate with experts in machine learning, molecular simulation, optimization, and bioinformatics, and interface with experimentalists generating large datasets via novel high-throughput assays. You will leverage in-house computational tools and contribute to the design, training, and evaluation of new machine learning-based methods. Depending on your assignment, this position may offer a hybrid schedule, blending in-person and virtual presence. You may have the flexibility to work from home one or more days per week. This position will be filled at either level based on knowledge and related experience as assessed by the responsibilities (outlined below) will be assigned if hired at the higher level. You will Collaborate with project scientists and engineers to develop, implement, and evaluate computational frameworks and models. Contribute to the development and application of advanced analysis methodologies; analyze data; document research through presentations and peer-reviewed publications. Support technical activities for new capability development and provide solutions to moderately complex to complex technical problems using established and innovative methods. Contribute to the completion of project milestones, influencing the development of organizational goals and objectives. Establish, implement, and maintain quality standards for project deliverables. Contribute to briefings and presentations documenting project activities and research results. Routinely interact with technical contacts at sponsor and partner organizations; represent the organization on specific technical projects. Participate in the development of future research directions and proposals to secure ongoing projects in computational protein design. Balance multiple projects/tasks and priorities to ensure deadlines are met, working independently with minimal direction within the scope of assignments. Perform other duties as assigned. Additional job responsibilities, at the SES.3 level Determine, propose, and implement advanced analysis methodologies and contribute to identifying future research directions and proposals that will secure future projects in the field. Guide the completion of projects and influence the development of organizational goals and objectives. Lead the development of briefings and presentations documenting to project activities and research results. Represent the organization as the primary technical contact on tasks and projects, serving on internal technical/advisory committees and potentially on external committees. Oversee the activities of other personnel, providing informal mentoring and guidance to less-experienced team members. Contribute to and influence the development of innovative projects, principles, and ideas in computational protein design. Qualifications Ability to secure and maintain a U.S. DOE Q-level security clearance which requires U.S. citizenship. Master’s degree in Machine Learning, Computational Biology, Statistics, Computer Science, Mathematics, or a related field, or the equivalent combination of education and related experience. Comprehensive knowledge and experience developing and applying algorithms in one or more of the following machine learning areas: deep learning, unsupervised feature learning, zero- or few-shot learning, active learning, transformer-based language modeling, multimodal learning. Experience developing and implementing deep learning models and algorithms using modern software libraries such as PyTorch, TensorFlow, or similar, as evidenced by publications or software releases. Experience with high-performance computing, multi-node, multi-GPU, distributed training. Comprehensive knowledge in protein and genome language models sufficient to communicate effectively with team members and subject matter experts. Proficient verbal and written communication skills necessary to collaborate within a team environment and present technical information to varied audiences. Effective interpersonal skills and initiative necessary to interact with all levels of personnel and work independently in a collaborative, multidisciplinary team environment. Demonstrated ability to balance multiple projects and prioritize competing demands while maintaining high-quality standards for deliverables. Additional qualifications at the SES.3 level Advanced knowledge and experience in developing and applying algorithms in machine learning areas. Significant experience developing and implementing medium to large-scale deep learning models and algorithms using modern software libraries. Demonstrated ability to provide guidance and informal mentoring to other personnel and junior team members. Advanced verbal and written communication skills necessary to effectively collaborate in a multidisciplinary team and present technical information to a variety of audiences. Demonstrated ability to represent the organization as a primary technical contact and to contribute to the development of innovative projects, principles, and ideas. Qualifications We Desire PhD in Computational Biology, Computational Bioengineering, Machine Learning, Statistics, Computer Science, Mathematics, or a related field. Strong understanding of protein and genome language models and datasets. Experience publishing research results in peer-reviewed scientific journals and presenting at conferences and workshops. Experience with GPU programming and running complex workflows. Pay Range $146,340 - $222,564 Annually $146,340 - $185,544 Annually for the SES.2 level $175,530 - $222,564 Annually for the SES.3 level This is the lowest to highest salary we in good faith believe we would pay for this role at the time of this posting. An employee’s position within the salary range will be based on several factors including, but not limited to, specific competencies, relevant education, qualifications, certifications, experience, skills, seniority, geographic location, performance, and business or organizational needs. Additional Information #LI-Hybrid Position Information This is a Career Indefinite position, open to Lab employees and external candidates. Why Lawrence Livermore National Laboratory? Included in 2026 Best Places to Work by Glassdoor! Flexible Benefits Package 401(k) Relocation Assistance Education Reimbursement Program Flexible schedules (*depending on project needs) Our values - visit https://www.llnl.gov/inclusion/our-values 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. Also, all L or Q cleared employees are subject to random drug testing. Q-level clearance requires U.S. citizenship. Pre-Employment Drug Test External applicant(s) selected for this position must 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. Wireless and Medical Devices Per the Department of Energy (DOE), Lawrence Livermore National Laboratory must meet certain restrictions with the use and/or possession of mobile devices in Limited Areas. Depending on your job duties, you may be required to work in a Limited Area where you are not permitted to have a personal and/or laboratory mobile device in your possession. This includes, but not limited to cell phones, tablets, fitness devices, wireless headphones, and other Bluetooth/wireless enabled devices. If you use a medical device, which pairs with a mobile device, you must still follow the rules concerning the mobile device in individual sections within Limited Areas. Sensitive Compartmented Information Facilities require separate approval. Hearing aids without wireless capabilities or wireless that has been disabled are allowed in Limited Areas, Secure Space and Transit/Buffer Space within buildings. How to identify fake job advertisements Please be aware of recruitment scams where people or entities are misusing the name of Lawrence Livermore National Laboratory (LLNL) to post fake job advertisements. 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