Generative Models Engineer

Company: Harvard University

Job Location: Cambridge, 02138

Category: Laboratory and Research

Type: Full-Time

Company Description: By working at Harvard University, you join a vibrant community that advances Harvard's world-changing mission in meaningful ways, inspires innovation and collaboration, and builds skills and expertise. We are dedicated to creating a diverse and welcoming environment where everyone can thrive.

Why join Harvard Medical School? Harvard Medical School's mission is to nurture a diverse, inclusive community dedicated to alleviating suffering and improving health and well-being for all through excellence in teaching and learning, discovery and scholarship, and service and leadership.

You'll be at the heart of biomedical discovery, education, and innovation, working alongside world-renowned faculty and a community dedicated to improving human health. This is more than a job - it's an opportunity to shape the future of medicine.

Job Description: Participate in the design of software that supports and enriches research productivity and reliability; implement software solutions. Develop software and data services with researchers to ensure that modern standards of reproducible code are kept.

Job-Specific Responsibilities: The Marks Lab in the Systems Biology Department at Harvard Medical School seeks an energetic, enthusiastic, and multi-talented individual to contribute broadly to our work using generative models to develop innovative solutions to biological and medical problems. We seek to build on transformational discoveries we have already made in the areas of protein structure prediction and design, prediction of changes in protein function resulting from human genetic variation, and predictions of complex formation between proteins, proteins and nucleic acids, and large and small molecules (e.g. drugs).

As the Generative Models Engineer, you will be the lead for addressing all lab needs for software development, database updates, cloud and GPU usage. You will also contribute to a key ongoing project in the lab in the area of vaccine design.

Specific duties: Lead software development and evaluation throughout the Marks lab. Lead new software development in collaboration with other researchers in the lab for all virus-related projects. Develop new end-to-end pipelines for software evaluations at scale. Develop performance metrics for software output and automate reports. Lead the implementation and maintenance of existing Marks lab collaborative software such as EVcouplings, ProteinGym, RNAgym, EVEscape and popEVE and their dependencies, and maintain GitHub.

Enable collaborations and train in-house and external collaborators in the use of Marks lab software. Co-lead and Coordinate ProteinDesign and ProteinGym collaborations (within lab and internationally). Create software documentation and training materials for collaborators and for onboarding new lab members.

Manage Marks lab compute needs. Anticipate and plan for large scale compute needs. Serve as primary point of contact for coordination with HMS RC on support needs such as GPU access, storage, database maintenance, containers, server running etc. Co-Lead funding proposals for compute resources (eg Nvidia, Amazon, Google etc).

Independent Research. Develop computational methods to identify optimal B cell epitope display for novel vaccine nanoparticle designs. Apply these methods to developing cross-strain and future-proofed influenza and coxsackie viruses, and other emerging threats. Communicate results in lab group meetings, research papers and potentially at conferences.

Qualifications: Basic Qualifications: Minimum of five years' post-secondary education or relevant work experience.

Additional Qualifications and Skills: Python ecosystem: NumPy, SciPy, pandas, scikit-learn, PyTorch/TensorFlow. Core computational biology & bioinformatics: strong domain knowledge of protein sequences, protein structure alignments, structure prediction and ideally familiarity with viral genomes. Familiarity with standard tools such as PyMOL and docking software AI and machine learning methods for sequences and structures: familiarity with AlphaFold, protein language models, ideally familiarity with GNNs, generative models and RF diffusions, protein MPNN Database, data pipelines experience with genomic, 3D protein and RNA structure data.

Additional Information: Term: This is a one-year term position from the date of hire, with the possibility of extension, contingent upon work performance and continued funding to support the position. Standard Hours/Schedule: 35 hours per week. Visa Sponsorship Information: Harvard University is unable to provide visa sponsorship for this position. Pre-Employment Screening: Identity. Work Format Details: This is a position that is based at a Harvard campus location with some remote work options available. Additional details will be discussed during the interview process. All remote work must be performed within one of the Harvard Registered Payroll States, which currently includes Massachusetts, Connecticut, Maine, New Hampshire, Rhode Island, Vermont, Georgia, Illinois, Maryland, New Jersey, New York, Virginia, Washington, and California (CA for exempt positions only). Certain visa types and funding sources may limit work location. Individuals must meet work location sponsorship requirements prior to employment.

Salary Grade and Ranges: This position is salary grade level 057. Please visit Harvard's Salary Ranges to view the corresponding salary range and related information.

Benefits: Harvard offers a comprehensive benefits package that is designed to support a healthy work-life balance and your physical, mental and financial wellbeing. Because here, you are what matters. Our benefits include, but are not limited to: Generous paid time off including parental leave. Medical, dental, and vision health insurance coverage starting on day one. Retirement plans with university contributions. Wellbeing and mental health resources. Support for families and caregivers. Professional development opportunities including tuition assistance and reimbursement. Commuter benefits, discounts and campus perks. Learn more about these and additional benefits on our Benefits & Wellbeing Page.