Overview:
In this role you will architect end-to-end computational drug discovery systems, turning modular ML components into scalable, reproducible workflows. You will define SOPs for model integration and data pipelines, and partner with platform teams to deploy workflows across gRED and pRED. You will translate research models into production-ready components with clear interfaces and benchmarks, driving adoption and trust. This is a chance to shape automated, cross-functional pipelines at Roche, accelerating medicines development. Compensation / Benefits Discretionary annual bonus Comprehensive benefits package Opportunities within a world-class biotech environment
Responsibilities:
Design computational workflow architectures that operationalize modular ML components into scalable, reproducible systems Lead the development and standardization of SOPs for model integration, data pipelines, and workflow execution across gRED and pRED Partner with platform engineering teams to implement workflows at scale Architect data integration with centralized data infrastructure (DDC), ensuring looped model-data-workflow processes Collaborate with the modeling team to translate research models into production-ready components with interfaces and benchmarks Navigate stakeholder environments to align on standards and drive adoption Lead and mentor engineers and scientists on workflow design, automation best practices, and architectural decisions
Key requirements:
PhD in relevant field or 8+ years building computational systems Deep expertise in workflow orchestration, data pipeline design, and software architecture Experience integrating heterogeneous data sources, models, and processes at scale Strong Python proficiency and experience with PyTorch, TensorFlow, or JAX Familiarity with workflow tools (Nextflow, Snakemake, Airflow) Software engineering practices (version control, testing, documentation, CI/CD) Experience in Life Sciences / Drug Discovery Track record translating research code into production systems Experience working across cross-functional stakeholders Strategic thinking Strong communication and ability to build consensus Cross-functional collaboration Python PyTorch TensorFlow
