2026 Summer Intern - Translational Safety

Roche • South San Francisco, CA

9d • $50 - $50 • Onsite

About The Position

2026 Summer Intern - Translational Safety Department Summary Development Sciences (DevSci) spans the entire drug discovery and development cycle — from early stage research to drug commercialization. Part of the drug development pipeline in DevSci includes the preclinical safety evaluation of candidate therapeutic molecules by toxicologists and pathologists in the Translational Safety (TS) department in order to enable further evaluation in humans. Within DevSci, the TS department ensures the safety of candidate molecules advancing through the pipeline by providing scientific insights. We support the vision of delivering the right drug in the right dose to the right patient. We are also committed to providing better outcomes for our people, patients, business, and communities by advancing and boldly championing diversity, equity, and inclusion in our work. The Digital Pathology team sits within TS and focuses on revolutionizing the analysis of histopathology slides. We advance drug development decision-making by providing state-of-the-art digital pathology solutions and computational analysis. We enable efficient pathology workflows and provide greater scientific understanding of toxicity and disease by integrating cutting-edge computational tools to support pathologist-driven interpretation of findings. This internship position is located in South San Francisco, on-site. The Opportunity Join our Digital Pathology team to help advance how we leverage data to support scientific decision-making. In this internship, you will join a project focused on applying advanced machine learning techniques to complex biological data. You will move beyond conventional analysis methods to develop robust and automated models that extract meaningful insights directly from raw data. Your work will help identify subtle patterns and trends that traditional approaches may overlook, enhancing our ability to make accurate and timely scientific assessments. You will also focus on model interpretability, ensuring that algorithmic results are transparent and actionable. You will sit at the intersection of data science and experimental science, building tools that allow researchers to clearly understand the evidence behind computational predictions.

Requirements

• Required Education: You meet one of the following criteria: Must be pursuing a Master's Degree (enrolled student). Must have attained a Master's Degree. Must be pursuing a PhD (enrolled student).
• Required Majors: Biomedical Engineering, Bioinformatics, Computer Science, Electrical Engineering, Computational Biology, Data Science, Applied Mathematics, Statistics, or a related field.
• Required Skills: Programming Proficiency: Strong experience in Python, including standard data science libraries (e.g., NumPy, Pandas, SciPy, Matplotlib).
• Machine Learning & Deep Learning: Familiarity with building and training models using frameworks such as PyTorch, TensorFlow, or Keras. Experience with time-series analysis or sequence modeling (e.g., RNNs, LSTMs, CNNs) is a strong plus.
• Signal Processing: Understanding of fundamental digital signal processing (DSP) concepts (e.g., filtering, Fourier transforms, wavelets).
• Data Handling: Experience working with large, complex datasets and performing data cleaning/preprocessing.
• Collaboration & Communication: Ability to work effectively in a cross-disciplinary team and clearly explain technical concepts to non-computational audiences (e.g., biologists, toxicologists).
• Scientific Curiosity: A genuine interest in applying computational methods to solve biological or medical problems.

Nice To Haves

• Excellent communication and interpersonal skills.
• Complements our culture and the standards that guide our daily behavior & decisions: Integrity, Courage, and Passion.

Responsibilities

• Develop Machine Learning Models: Design and implement algorithms in Python to analyze complex datasets and improve predictive performance.
• Enhance Model Interpretability: Create methods to visualize and explain model outputs, ensuring transparency for scientific users.
• Data Processing: Apply advanced computational techniques to clean, normalize, and prepare data for analysis.
• Collaborate Cross-Functionally: Work closely with domain experts to validate findings and integrate computational insights into scientific workflows.
• Present Results: Communicate technical findings clearly to diverse audiences, translating complex data into actionable scientific knowledge.

Benefits

• Intensive 12-weeks, full-time (40 hours per week) paid internship.
• Program start dates are in May/June 2026.
• A stipend, based on location, will be provided to help alleviate costs associated with the internship.
• Ownership of challenging and impactful business-critical projects.
• Work with some of the most talented people in the biotechnology industry.
• paid holiday time off benefits

What This Job Offers

• Job Type: Full-time
• Career Level: Intern
• Number of Employees: 5,001-10,000 employees