2026 Summer Intern - Computational Sciences Center of Excellence - AI systems performance engineering

2026 Summer Intern - Computational Sciences Center of Excellence - AI systems performance engineering

Department Summary

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 this 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.

Within the CoE organisation, the Data and Digital Catalyst organisation drives the modernisation of our computational and data ecosystems and integration of digital technologies across Research and Early Development to enable our stakeholders, power data-driven science and accelerate decision-making.

This internship position is located in South San Francisco, on-site.

The Opportunity

We’re seeking a PhD/Master’s student with expertise and passion for performance-aware scientific computing, particularly in machine learning systems. In this role, you will focus on optimizing GPU-accelerated workloads and collaborate with ML scientists and engineers to accelerate AI-driven discovery by improving training and inference efficiency.

Key Responsibilities:

• Assist in optimizing GPU-accelerated workloads, with a focus on throughput, latency, and memory efficiency.

• Profile and analyze machine learning training and inference workloads to identify performance bottlenecks, and apply optimizations at the graph, kernel, and system levels (e.g., PyTorch Profiler, NVIDIA Nsight).

• Design and evaluate performance-aware algorithms that scale on multi-node clusters.

• Develop and optimize high-performance GPU kernels, and make trade offs to maximize hardware utilization.

• Develop a deep understanding of hardware features and performance characteristics to optimize large-scale AI and scientific computing workloads.

• Support benchmarking and performance testing efforts for AI systems at scale.

Program Highlights

• Intensive 12-weeks full-time (40 hours per week) paid internship.

• Program start dates are either May.18 2026 or June. 1st 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.

Who You Are

Required Education

Must be pursuing a PhD/Master’s (enrolled student).

Required Majors

Computer Sciences, Artificial Intelligence, Computational Sciences or a related field with a focus on machine learning systems, parallel computing, compilers or similar.

Required Skills 

• Programming proficiency in C/C++ and Python.

• Experience writing and optimizing scientific computing kernels with CUDA or similar.

• Understanding of GPU or other accelerator architectures.

• Communication: A collaborative mindset and enthusiasm for bridging Computer Systems, Machine Learning Engineering and Biology.
   

Preferred K