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Principal Scientist, SOP & Workflow Automation Champion, AI for Drug Discovery (AIDD)

Roche•South San Francisco, CA
•$201,300 - $373,800•Remote
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About The Position

Advances in AI, data, and computational sciences are transforming drug discovery and development. Roche’s Research and Early Development organisations at Genentech (gRED) and Pharma (pRED) have demonstrated how these technologies accelerate R&D, leveraging data and novel computational models to drive impact. Seamless data sharing and access to models across gRED and pRED are essential to maximising these opportunities. The new Computational Sciences Center of Excellence (CoE) is a strategic, unified group whose goal is to harness the transformative power of data and Artificial Intelligence (AI) to assist our scientists in both pRED and gRED to deliver more innovative and transformative medicines for patients worldwide. At Roche's AI for Drug Discovery (AIDD) group within the Computational Sciences Center of Excellence, we are architecting a vision for end-to-end computational drug discovery. Today, drug discovery workflows are fragmented—different models for different modalities, disconnected processes across teams, manual handoffs between discovery and development. We are building a unified, modular system where machine learning methods integrate seamlessly into executable, agentic workflows that empower scientists across our organization to discover better medicines faster. This is a critical moment. We have developed novel machine learning capabilities for large molecule discovery, but translating those capabilities into scalable, operationalized workflows at the organizational level requires both scientific credibility and strategic engineering acumen. We're looking for an exceptional Principal Scientist who can architect how our computational models become standard operating procedures (SOPs) and automated workflows that portfolio teams actually use, depend on, and trust. Drug discovery is moving toward end-to-end computational pipelines. Today, our ML methods exist in silos—powerful but disconnected from operational workflows. The scientist who can bridge that gap—who designs the systems that make models actionable, scalable, and trustworthy—will fundamentally accelerate how medicines are discovered. That's this role.

Requirements

  • PhD in Computer Science, Computational Biology, Bioinformatics, or related field, or equivalent advanced experience (8+ years building computational systems)
  • Deep expertise in workflow orchestration, data pipeline design, and software architecture (not just machine learning)
  • Proven experience designing systems that integrate heterogeneous data sources, models, and processes at scale
  • Strong proficiency in Python and modern ML frameworks (PyTorch, TensorFlow, JAX); familiarity with workflow tools (Nextflow, Snakemake, Airflow, or similar)
  • Understanding of software engineering practices: version control, testing, documentation, CI/CD pipelines
  • Demonstrated experience working at the intersection of computational methods and experimental biology
  • Understanding of drug discovery workflows: what scientists actually need, where handoffs break down, how to design for usability
  • Track record of translating research code into production systems that real teams use
  • Experience working across technical and non-technical stakeholders (biology, chemistry, engineering)
  • Proven ability to lead complex, cross-functional initiatives involving multiple teams and organizations
  • Track record of driving adoption of new standards, tools, or processes in larger organizations
  • Strong communication skills: can explain complex technical concepts to diverse audiences and build consensus
  • First-author publications or equivalent evidence of research contributions
  • You see the gap between "research works in a paper" and "research works at scale in an organization"
  • You understand how to design systems for reliability, debuggability, and adoption
  • You can balance scientific rigor with pragmatic engineering constraints

Responsibilities

  • Design computational workflow architecture that operationalizes modular ML components into scalable, reproducible, and agentic-ready systems
  • Lead the development and standardization of SOPs for model integration, data pipelines, and workflow execution across gRED and pRED
  • Partner strategically with Roche's platform engineering teams to implement workflows at scale
  • Architect data integration with Roche's centralized data infrastructure (DDC), ensuring seamless model-data-workflow loops
  • Collaborate with the modeling team to translate research-stage models into production-ready components with clear interfaces, performance benchmarks, and failure modes
  • Navigate complex stakeholder environments, including portfolio teams, platform organizations, and technology development groups, to align on standards and drive adoption
  • Lead and mentor engineers and scientists on workflow design, automation best practices, and computational architecture

Benefits

  • Discretionary annual bonus may be available based on individual and Company performance.
  • Benefits detailed at the link provided below.

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What This Job Offers

Job Type

Full-time

Career Level

Principal

Education Level

Ph.D. or professional degree

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