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Phd Machine Learning Drug Discovery Jobs (NOW HIRING)

Cellarity

Machine Learning Scientist

Somerville, MA · On-site

... drug discovery. This role involves hands-on modeling of high-dimensional biological data ... PhD in Computer Science, Computational Biology, or related field, OR Master's degree with 3+ years ...

Cellarity

Machine Learning Scientist

Somerville, MA · On-site

... drug discovery. This role involves hands-on modeling of high-dimensional biological data ... PhD in Computer Science, Computational Biology, or related field, OR Master's degree with 3+ years ...

SupportFinity™

Machine Learning Engineer

Boston, MA · On-site

... machine learning, and proprietary big data infrastructure. We are expanding the team of talented ... drug discovery with creativity and innovation. About the Role We are seeking a high-impact Machine ...

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Phd Machine Learning Drug Discovery information

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How much do phd machine learning drug discovery jobs pay per year?

As of Sep 10, 2026, the average yearly pay for phd machine learning drug discovery in the United States is $42,584.00, according to ZipRecruiter salary data. Most workers in this role earn between $32,500.00 and $46,000.00 per year, depending on experience, location, and employer.

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Infographic showing various Phd Machine Learning Drug Discovery job openings in the United States as of August 2026, with employment types broken down into 1% As Needed, 75% Full Time, 23% Part Time, and 1% Contract. Highlights an 84% Physical, 2% Hybrid, and 14% Remote job distribution, with an average salary of $42,584 per year, or $20.5 per hour.

Principal Machine Learning Engineer

New York, NY • On-site

Genentech, Inc.
Scientific Research and Development Services • 10K+ employees
Learn more about Genentech, Inc.external

Full-time

Posted 6 days ago

Genentech rating

8.8

Company rating: 8.8 out of 10

Based on 22 frontline employees who took The Breakroom Quiz

12th of 86 rated pharmaceutical

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 organizations 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 maximizing 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 (Prescient Design), we are revolutionizing drug discovery with cutting-edge machine learning. We are seeking a Principal Machine Learning Engineer to join our Foundation Models team. In this role, you will drive the engineering, scaling, and operationalization of our internal reasoning Large Language Models (LLMs) and agentic systems, enabling them to succeed at complex biomolecular design and autonomous scientific workflows.
You will work at the intersection of engineering and research, spanning the full stack: from the agent orchestration logic that makes these systems scientifically useful, to the distributed infrastructure and MLOps/AgentOps that make them robust at scale.
In this role, you will:
  • Agentic Systems Engineering: Architect and deploy autonomous agents that utilize tools, retrieve scientific evidence, and execute multi-step reasoning across drug discovery workflows. Design and implement advanced agent memory architectures and context management for long-horizon scientific tasks. Build reliable interfaces between agents and genomic, chemical, and clinical data sources.
  • Scalable ML Systems & Productionization: Design, build, and optimize large-scale distributed training and inference systems for foundation models. Own the production Python/PyTorch codebases that turn fast-moving research into enterprise-grade software.
  • Own the MLOps/AgentOps Stack: Establish best practices for the full lifecycle: experiment tracking, system observability and monitoring, rigorous evaluation harnesses (checking agent output against scientific ground truth), CI/CD, and infrastructure.
  • Technical Strategy & Leadership: Define the long-term engineering roadmap for AI4DD's agentic and foundation models. Serve as a technical authority on ML infra for Genentech leadership, architect cross-functional platforms, and elevate the engineering bar across gRED.
  • Research-to-Production Translation: Partner closely with ML Scientists and domain experts to translate open-ended scientific problems and complex reasoning objectives into scoped, shippable, and highly efficient systems.

Who You Are:
  • Education & Experience: BS, MS, or PhD in Computer Science, Machine Learning, Engineering, or a related quantitative field. You have a demonstrated track record of technical leadership with increasing levels of experience based on degree: PhD with 5+ years, MS with 8+ years, or BS with 10+ years of industry experience building, shipping, and owning large-scale ML systems and infrastructure end-to-end.
  • Engineering Foundation: Exceptional Python programming skills and rigorous software engineering fundamentals (Git, automated testing, CI/CD, documentation, architecture design).
  • Deep Learning & Distributed Systems: Extensive hands-on experience with modern deep learning frameworks (PyTorch, JAX) and deploying ML infrastructure on AWS or HPC environments, including distributed training tools.
  • Agentic Systems: Practical experience designing agent orchestration frameworks (e.g., LangGraph, MCP-based tool integration), managing persistent agent memory, and building self-improving loops.
  • Domain Interest: Strong passion for applying frontier AI and agentic science to AI for Drug Discovery (AI4DD), biology, and chemistry.

Preferred Qualifications:
  • Inference-Time Optimization: Deep expertise in LLM serving, test-time compute, sampling/search strategies, model routing, batching, caching, and latency/cost/quality tradeoffs.
  • Scientific Context: Experience working with molecular modalities (e.g., protein sequences, chemical graphs, and structured molecular data).
  • Open Source: A public portfolio of significant technical contributions to open-source ML, systems, or MLOps libraries.

Onsite presence on our campus is expected in compliance with Roche/Genentech policy.
Relocation benefits are not available for this job posting.
The expected salary range for this position based on the primary location of New York is $192,500 - $357,500. 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
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.
If you have a disability and need an accommodation in relation to the online application process, please contact us by completing this form Accommodations for Applicants.

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About Genentech

Sourced by ZipRecruiter

A member of the Roche Group, Genentech has been at the forefront of the biotechnology industry for more than 40 years, using human genetic information to develop novel medicines for serious and life-threatening diseases. Genentech has multiple therapies on the market for cancer & other serious illnesses. Please take this opportunity to learn about Genentech where we believe that our employees are our most important asset & are dedicated to remaining a great place to work.

Industry

Scientific research and development services

Company size

10,000+ Employees

Headquarters location

South San Francisco, CA, US

Year founded

1976

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