About the job
We are seeking a Scientist to join a dynamic team applying computational and data-driven approaches to design novel antigens and evaluate vaccine responses. At Apriori Bio, we have built our biology-enabled AI/ML platform Octavia™ to map viral fitness landscapes, predict immune escape trajectories, and design vaccines to elicit an optimal protective response to future challenges. The successful candidate will contribute to the development and application of computational methods—including structural modeling, machine learning, and statistical inference— that build on Octavia™ insights to support the company’s vaccine design efforts. Experience working with protein engineering and immunological datasets is valuable, as is interest or training in high-throughput functional genomics, virology, or vaccinology.
Responsibilities
Use high-dimensional datasets (e.g., mutational scanning, NGS-based phenotypic screens) and computational approaches—including structural modeling, sequence analysis, and machine learning—to support antigen and immunogen engineering
Apply and refine computational models that integrate screening data with structural and sequence-based features to guide biomolecule engineering
Generate hypotheses and design analyses that inform evaluation of novel immunization strategies (in partnership with experimental teams)
Identify, evaluate, and curate external datasets relevant to vaccine design and antigen optimization
Develop reproducible computational tools and pipelines for data-driven product design that can be integrated into broader platform workflows
Collaborate with engineers, computational scientists, and experimentalists to execute project goals and ensure smooth integration of computational findings
Work with immunology and characterization teams to interpret vaccine immunogenicity using next-generation immune monitoring and antibody profiling tools
Clearly communicate results and insights to colleagues across scientific and cross-functional teams
Tackle difficult scientific problems with creativity and resilience while iterating quickly as new data emerges
Take ownership of complex, open-ended scientific questions, driving analyses forward through ambiguity while balancing speed, rigor, and collaborative problem-solving
Qualifications
Minimum
Ph.D. in bioengineering, immunology, computational biology, or a related field (or equivalent research experience)
Experience applying data-driven protein engineering methods to predict and affect protein function, including structural and sequence-based machine-learning approaches
Experience or training in antigen design leveraging vaccine immune response readouts
Solid statistical and quantitative foundation, with the ability to translate complex biological datasets into actionable insights
Strong scientific programming skills (Python preferred) and familiarity with best practices in reproducible scientific computing
Experience working with high-throughput biological data or NGS-based assays, including modeling and statistical analysis
Experience using version control and collaborative coding workflows
Preferred
Experience in vaccine design
Experience with deep learning approaches
Exposure to antibody characterization, immune profiling, or immune repertoire analyses
Experience with NGS data pipelines, from processing to interpretation
Familiarity with cloud or distributed computing environments
Familiarity with storage, analysis and transformation of large tabular datasets and relational databases
Laboratory experience in molecular biology, biochemistry, or immunology (a plus, not required)