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Postdoctoral Computational Drug Discovery Jobs (NOW HIRING)

Novartis

Computational Medicinal Chemist

San Diego, CA · On-site

$138K - $257K/yr

We are a driving force behind drug discovery, and we are now eagerly searching for an exceptional computational scientist like you to join our ranks. Imagine the opportunity to unlock hidden ...

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Postdoctoral Computational Drug Discovery information

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$25K

$59K

$83.5K

How much do postdoctoral computational drug discovery jobs pay per year?

As of Aug 20, 2026, the average yearly pay for postdoctoral computational drug discovery in the United States is $59,022.00, according to ZipRecruiter salary data. Most workers in this role earn between $49,000.00 and $66,500.00 per year, depending on experience, location, and employer.

What is a postdoctoral computational drug discovery researcher?

A Postdoctoral Computational Drug Discovery researcher is a scientist with a doctoral degree who applies computational methods and modeling to identify, design, and optimize potential drug candidates. They use techniques such as molecular docking, virtual screening, and machine learning to predict how compounds will interact with biological targets. Their work accelerates the drug discovery process by helping to focus laboratory experiments on the most promising leads. These researchers often collaborate with experimental scientists and contribute to publications and grant proposals.

What are some typical collaborative projects a postdoctoral computational drug discovery researcher might work on?

As a Postdoctoral Computational Drug Discovery researcher, you will often collaborate with interdisciplinary teams that include medicinal chemists, biologists, and data scientists. Projects typically involve integrating computational modeling with experimental data to identify and optimize potential drug candidates. You may work on tasks such as virtual screening, molecular dynamics simulations, and structure-based drug design, frequently presenting findings in group meetings and co-authoring publications. This collaborative environment fosters both scientific innovation and professional growth.

What are the key skills and qualifications needed to thrive as a postdoctoral computational drug discovery scientist, and why are they important?

To thrive as a Postdoctoral Computational Drug Discovery scientist, you need a strong background in computational biology, chemistry, or bioinformatics, usually supported by a PhD in a relevant field. Expertise in molecular modeling software, scripting languages (such as Python or R), and familiarity with drug discovery databases and high-performance computing are commonly required. Analytical thinking, problem-solving, and effective communication are vital soft skills for interdisciplinary collaboration and presenting complex findings. These skills are crucial to efficiently design and analyze experiments, accelerate drug discovery pipelines, and contribute valuable insights in a competitive research environment.
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Postdoctoral Computational Drug Discovery jobs near you
Infographic showing various Postdoctoral Computational Drug Discovery job openings in the United States as of August 2026, with employment types broken down into 1% As Needed, 76% Full Time, 20% Part Time, 2% Contract, and 1% Nights. Highlights an 96% Physical, 1% Hybrid, and 3% Remote job distribution, with an average salary of $59,022 per year, or $28.4 per hour.

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

F. Hoffmann-La Roche AG

South San Francisco, CA • On-site

$201 - $374/hr

Other

Posted 12 days ago

Job description

A healthier future. It’s what drives us to innovate. To continuously advance science and ensure everyone has access to the healthcare they need today and for generations to come. Creating a world where we all have more time with the people we love. That’s what makes us Roche. 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.

The Opportunity 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.

In this role, you will:
  • 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 modesNavigate 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
Who you are
  • Technical Foundation
    • 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
    • Experience in Life Sciences / Drug Discovery
    • 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)
  • Leadership & Collaboration
    • 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
  • Relocation benefits are NOT available for this job posting.

The expected salary range for this position, based on the primary location of California, is $201,300 - 373,800. Actual pay will be determined based on experience, qualifications, geographic location, and other job-related factors permitted by law. A discretionary annual bonus may be available based on individual and Company performance.

This position also qualifies for the benefits detailed at the link provided below.

Benefits #ComputationCoE #tech4lifeComputationalScience #tech4lifeAI

Genentech is an equal opportunity employer.

It is our policy and practice to employ, promote, and otherwise treat any and all employees and applicants on the basis of merit, qualifications, and competence.

The company's policy prohibits unlawful discrimination, including but not limited to, discrimination on the basis of Protected Veteran status, individuals with disabilities status, and consistent with all federal, state, or local laws.

Genentech is an equal opportunity employer.

It is our policy and practice to employ, promote, and otherwise treat any and all employees and applicants on the basis of merit, qualifications, and competence.

The company's policy prohibits unlawful discrimination, including but not limited to, discrimination on the basis of Protected Veteran status, individuals with disabilities status, and consistent with all federal, state, or local laws.

Who We Are Genentech, a member of the Roche group and founder of the biotechnology industry, is dedicated to pursuing groundbreaking science to discover and develop medicines for people with serious and life-threatening diseases. To solve the world's most complex health challenges, we ask bigger questions that challenge our industry and the boundaries of science to transform society. Our transformational discoveries include the first targeted antibody for cancer and the first medicine for primary progressive multiple sclerosis. The next step is yours.

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