Computational Enzyme Engineer

Computational Enzyme Engineer

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 dedicated to fostering a culture that values individuals, talents, partnerships, ideas, experiences, and different perspectives, recognizing their importance to the continued success of the Laboratory's mission.

Pay Range

$140,700 - $214,032

$140,700 - $178,392 Annually for the SES.2 level

$168,780 - $214,032 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; pay will not be below any applicable local minimum wage. 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.

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.

Job Description

We have an opening for a Computational Enzyme Engineer to join ongoing projects developing innovative biologics, therapeutics, and vaccines against infectious diseases as part of the Center for Predictive Bioresilience (CPB). CPB is an exciting and fast-paced engineering center combining predictive computational modeling, machine learning, and experimental biology to develop medical countermeasures.

You will be responsible for leveraging cutting-edge computational tools and algorithms like RFDiffusion, RosettaFold, ProteinMPNN, and AlphaFold to design and engineer proteins and enzymes with enhanced activity, specificity, and stability. This role involves building and training predictive models for enzyme engineering using machine learning and deep learning tools, as well as performing extensive data analysis on experimental protein engineering datasets (including activity and sequence/structure information) to continuously refine and improve these models. You will also develop and implement computational methods, utilizing 3D structures and/or sequences, to optimize enzyme characteristics, and will design protein libraries and datasets for high-throughput screening. Collaboration with interdisciplinary teams of biologists, chemists, and other engineers is crucial to integrate computational insights seamlessly with experimental design and execution. This position will be in the Computational Engineering Division (CED), within the Engineering Directorate, matrixed to the CPB.

In this role, you will

• Utilize advanced computational tools and algorithms (such as RFDiffusion, RosettaFold, ProteinMPNN, and AlphaFold) to contribute to and actively participate in the design and engineering of novel proteins and enzymes with desired properties (e.g., enhanced activity, specificity, or stability).
• Collaborate with team members and participate in building and training predictive models for enzyme engineering using techniques like machine learning and deep learning, including providing input, recommending enhancements, and solving problems of moderate complexity.
• Analyze large datasets from experimental protein engineering efforts (including enzyme activity data and sequence/structure information) to refine and improve computational models.
• Develop and implement moderately complex computational methods, potentially utilizing 3D structures and/or sequences as input to enhance enzyme stability, solubility, and activity.
• Document methods and implementation methodologies, activities, sequences, and requirements in both informal and formal reports and presentations.
• Design protein libraries and datasets for high-throughput screening and hit identification.
• Analyze data, deliver results, engage with, and participate in a talented team to identify, create, implement, benchmark, and scale cutting-edge techniques that integrate biophysics and AI for computational protein design, with a focus on therapeutic modalities including antibodies.
• Design, test, deploy, and maintain high-quality pipelines on HPC and cloud infrastructures, ensuring scalable and robust solutions.
• Balance multiple projects/tasks and priorities of customers and partners to ensure deadlines are met, while working independently with limited direction within the scope of the assignment.
• Perform other duties as assigned.

Additional job responsibilities at the SES.3 level

• Lead projects that develop advanced computational protein design strategies to meet diverse scientific and technical challenges.
• Independently determine the appropriate technical objectives, criteria, and approaches to satisfy and execute project deliverables.
• Provide solutions to abstract and complex problems using in-depth analysis, drawing from advanced level technical knowledge and best practices, and collaborate in the development of innovative methods/technology to guide and ensure successful completion of project and organizational goals.
• Represent the organization as the primary technical contact by sharing relevant knowledge, providing opinions and recommendations, and exerting influence to fulfill deliverables as a team.
• Lead and mentor junior staff and students.

Qualifications

• Master's degree in biochemistry, biophysics, bioinformatics, computational chemistry, computer science, AI/ML, or a related technical discipline focused on solving biological problems using computational approaches, or the equivalent combination of education and related experience.
• Comprehensive knowledge of or experience in developing and implementing novel methods and algorithms for computational protein design.
• Proficiency in programming with languages such as Python, C/C++, or Java.
• Proficient written, and verbal communication skills necessary to work and collaborate effectively in a multi-disciplinary environment, and to present and explain technical information.
• Demonstrated strong record of documentation of executed work.
• Ability to prioritize, balance, and keep several parallel threads of work in simultaneous, smooth motion.

Additional qualifications at the SES.3 level

• Advanced level knowledge and significant experience in computational protein design or a related technical field.
• Ability to independently develop and execute complex analyses and to prepare and finalize tailored reports.
• Significant experience leading interdisciplinary teams, including setting clear expectations, delegating to subordinates and peers, and ensuring successful, timely completion of objectives.
• Advanced verbal and written communication, facilitation, and interpersonal skills necessary to effectively collaborate and lead in a team environment and to present and explain technical information, influence and guide team members, and provide advice to management.

Qualifications We Desire

• PhD in biochemistry, biophysics, bioinformatics, computational chemistry, computer science, AI/ML, or a related technical discipline focused on solving biological problems using computational approaches.
• Demonstrated understanding of fundamental biochemistry and enzyme engineering concepts including kinetics, reaction mechanisms, transition states, and rational design.
• Experience with classic computational approaches to estimating enzymatic properties such as thermodynamic stability and net charge and mathematical modeling of enzyme reaction dynamics.
• Familiarity with non-canonical amino acids and their use in protein design.
• Experience with design and modeling tools such as Rosetta, RFDiffusion, ProteinMPNN, AlphaFold, etc.
• Background in developing generative AI, structure-, or sequence-based methods for therapeutic protein design.
• Familiarity with training generative AI models, applying bioinformatics approaches to sequence analysis, and managing large databases of protein structures and sequences.
• Experience with modern deep learning frameworks (e.g., PyTorch, JAX, SageMaker) and cloud services (e.g., Git, Docker, AWS Batch, Step Functions, EKS).