Develop and improve machine learning models for molecular and structural biology to support drug design workflows. Collaborate with internal teams and external partners to build robust benchmarking strategies and resolve data quality issues.
The working student will support core finance processes including accounts payable, expense management, and payroll cycles. They will also assist with audit preparation and maintain financial documentation to ensure operational efficiency.
You will own the marketing strategy, narrative, and end-to-end campaigns to position the company's AI-driven drug discovery platform to scientific leaders. This involves translating complex scientific benchmarks into compelling arguments and managing a small team to execute across various channels.
You will lead the expansion into predictive toxicology and quantitative biology by setting the scientific strategy and integrating workflows into the federated platform. You will also act as a scientific lead for customers and partners to drive the adoption of models in real drug-discovery pipelines.
Design and implement scalable drug discovery workflows in Python to automate the drug design process. Collaborate cross-functionally with scientists and engineers to translate scientific problems into production-grade software.
Build and implement ML applications for structural biology, focusing on fine-tuning foundational models like OpenFold and ESMFold. Design scalable pipelines for training, inference, and deployment while collaborating with customers to solve real-world drug discovery problems.
The Security Lead will define and scale Apheris' security capabilities, owning cloud, application, and corporate security. This includes managing incident response, vulnerability remediation, and maintaining compliance frameworks like ISO 27001 and SOC 2.
Define and own the binding-data preparation protocol and build the modular tooling and pipelines to standardize heterogeneous pharma data. Collaborate with pharmaceutical partners to harmonize assay metadata and curate public binding-data foundations for ML model training.
Lead the delivery and operationalization of large-scale ML systems for antibody modeling and biologics discovery. Translate complex scientific goals into executable technical plans while mentoring senior engineers and ML scientists.
