Senior/ Computational Biologist/ ML Scientist

Kernal Biologics
Boston, MAPosted 21 February 2026

Job Description

<p>September 24, 2025</p> <p><strong>Position:</strong><strong>  </strong><strong> <br>Senior Computational Biologist/ ML Scientist <br><br>Company Overview:</strong><strong>  </strong><strong> <br></strong>At Kernal Bio, we engineer human cells inside the body using selective mRNA-LNP technology to treat cancer and autoimmune diseases. Our AI-designed mRNAs, delivered via antibody-decorated lipid nanoparticles (LNPs), ensure precise targeting and minimize off-target effects. Our KR-402 program focuses on hematological malignancies and autoimmune disorders, achieving >90% in vivo delivery efficiency with T-cell-targeted LNPs. The KR-335 program, targeting solid tumors resistant to standard immunotherapies, shows a 100x therapeutic index with strong efficacy and tolerability in preclinical models, including mice and non-human primates.</p> <p>With roots at MIT, Harvard, Merck, and BMS, our management team has spearheaded the development of three FDA-approved therapies and holds over 120 patents. Based in Cambridge, MA, and backed by Hummingbird Ventures, Amgen Ventures, NVIDIA, and NASA, Kernal Bio is revolutionizing mRNA therapeutics by leveraging AI to deliver precision treatments within the body. </p> <p><strong>Job Summary <br></strong></p> <p><strong>Senior Computational Biologist/ ML Scientist </strong><br>We are seeking a Senior Computational Biologist to join our dynamic RD team and spearhead our drug discovery efforts. This is a chance to move beyond routine data analysis and creatively apply advanced computational methods to solve complex biological challenges. You will be a key driver in designing next generation mRNA therapeutics, directly impacting our pipeline and scientific strategy.</p> <p><strong>The Role</strong><br>As a core member of our research group, you will leverage your expertise to extract meaningful insights from diverse datasets. Your primary focus will be on applying sophisticated data science and machine learning techniques to biological questions. You’ll collaborate closely with diverse cross-functional teams, including our wet lab scientists, synthetic biology, and formulation teams, acting as a bridge between data and discovery.</p> <p><strong>Key responsibilities include:</strong></p> <p>Data Analysis Modeling: Design and execute rigorous statistical and computational analyses by integrating and analyzing diverse omics datasets, including genomics, transcriptomics, proteomics, and single-cell sequencing data. You will also be responsible for developing, training, and deploying novel AI/machine learning models to predict biological outcomes and accelerate drug discovery.</p> <p>High-Performance Computing: Efficiently manage and scale computational workloads by leveraging cloud computing platforms and utilizing NVIDIA GPU-accelerated computing for data processing and model training.</p> <p>Scientific Communication: Prepare scientific manuscripts, contribute to intellectual property filings, and present your findings at leading scientific conferences and internal meetings.</p> <p>Documentation Workflow: Meticulously document all computational workflows, experimental results, and analyses in an Electronic Lab Notebook (ELN) such as Benchling and maintain rigorous version control and facilitate collaborative code development using platforms like GitHub.</p> <p>Mentorship: Guide and mentor junior team members, sharing your knowledge of best practices in computational analysis and scientific inquiry.</p> <p><strong>Who We’re Looking For</strong><br>We are looking for a creative and detail-oriented scientist with a proven track record. You should have a Ph.D. in Computational Biology, Bioinformatics, or a related field, along with a minimum of 3 years of experience in the biopharmaceutical industry.</p> <p>You are a master of your craft with:</p> <p>Deep Expertise: A strong background in the analysis of high-throughput biological data, including genomics, transcriptomics, proteomics, and single-cell s ... (truncated, view full listing at source)