GraalVM

A high-performance runtime supporting Java and JVM languages, JavaScript, Ruby, R, Python, C/C++, and more. It can run standalone or embedded in OpenJDK / OracleJDK, Node.js, and Oracle Database.

Learn more about the project

Possible Research Areas

• Improve the code inlining efficiency for the GraalVM Just-In-Time (JIT) compiler
• Reduce compilation time via intermediate Representations (IR) improvements for the GraalVM JIT compiler
• Add a new high-throughout and low-footprint Garbage Collector (GC) for GraalVM Native Image (Ahead-Of-Time compiler)
• Investigate reduction of the compilation time of GraalVM Native Image
• Create better tooling support for polyglot programming and other GraalVM features
• Optimize the GraalVM WebAssembly runtime for popular frameworks and libraries
• Improve the peak performance for code with frequent native calls in the GraalVM LLVM runtime
• Help us to improve the Micronaut micro-service framework
• Implement support for Scala and Kotlin for the Micronaut framework

Point of Contact

To apply, please send an email with the required information (see How to Apply above) to graalvm-internships_ww_grp@oracle.com.

Oracle Database Multilingual Engine

Stored procedures, user-defined functions and triggers - in general server-side procedural logic - complement SQL processing and are an important part of many database applications, in particular in enterprise environments. Multilingual Engine (MLE) is a feature of Oracle Database® that enables the use of modern programming languages like JavaScript and Python for server-side procedural logic. On the basis of GraalVM, MLE builds a platform for efficient, scalable program execution in Oracle® Database that is tightly integrated with processing of relational and non-relational data.

The Multilingual Engine team at Oracle Labs works on further developing this platform to provide innovative features for database apps. Internships in the Multilingual Engine team offer the opportunity to work with state-of-the-art database and virtual machine technology, and have a direct impact on the world's premier database system.

Learn more about the project

Potential Topics

• WebAssembly for In-Database Applications
• Co-Optimizing JavaScript and SQL Queries
• Machine learning using Python in the Oracle® Database

Point of Contact

To apply, please send an email with the required information (see How to Apply above) to Labs-Hiring_ww@oracle.com.

Oracle Labs Apps

The Oracle Labs Apps team is in charge of designing, building and operating apps that follow the principles of modern app development.

The team developers apps that are used internally as well as apps that improve the developer experience of people who interact with Oracle's open-source projects. One such project is the Oracle Contributor Agreement Signing Service (OCASS). OCASS enables contributors to Oracle-sponsored open-source projects to sign the Oracle Contributor Agreement (OCA), a document which gives Oracle and the contributor joint copyright interests in the contributed code. All apps are developed and operated to adhere to high standards in terms of security, compliance, availability, and more.

Learn more about Oracle Labs Apps

Potential Topics

• Development of various features spanning the entire app stack
• Leverage database-centric architectures to simplify the app stack (e.g., transactional event queues for message querying)
• Observing business metrics

Point of Contact

To apply, please send an email with the required information (see How to Apply above) to Labs-Hiring_ww@oracle.com.

Automating Machine Learning and Explainability (AutoMLx)

Accurate, fast, and easy-to-use automated machine learning pipeline with integrated explainability techniques.

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Possible Research Areas

• AutoML and/or explainability for classification, regression, anomaly detection, and forecasting tasks
• Explore support for federated learning
• Explore techniques to reduce model bias while tuning
• Extend dataset support for unstructured (e.g., NLP) and semi-structured (e.g., video/audio/graph) data
• Generic model support including GNNs, DNNs and/or RNNs

Point of Contact

To apply, please send an email with the required information (see How to Apply above) to Labs-Hiring_ww@oracle.com.

Scalable Graph Analytics and Machine Learning

Graph analytics is a powerful tool to efficiently leverage latent information stored inside data connections. As the number of connections grows exponentially in today’s increasingly common Big Data, being able to process graphs at scale becomes increasingly relevant. At Oracle Labs, we are developing scalable graph-processing solutions that cover a wide range of customer needs and applications:

• PGX: a standalone graph analytical system that supports graph algorithms, such as PageRank, graph queries with PGQL (an SQL-like graph query language), and graph ML. PGX includes both a single-machine in-memory engine and a distributed engine for very large graphs, and is already available as an option in Oracle products and an active research project at Oracle Labs. Learn more about PGX
• Graph-in-DB: scalable graph processing support in the Oracle Database. Graph-in-DB is an ambitious project which leverages knowledge from various domains of computer science, such as databases, graphs, algorithms and data structures, tuning and performance, multicore and distributed computing, machine learning, and compilation. It involves significant research and design effort, as well as challenging engineering tasks. The Graph-in-DB project is also a great opportunity to gain unique software development experience as it takes place in an exceptionally large and complex system.
• Domain Global Graphs: for enterprise use cases, organizations adopt the graph data model to integrate various data sources in one global view from their enterprise domain (e.g., financial), so that they can run graph analytics and conduct investigations. The team integrates PGX and Data Studio into solutions that support the investigation of domain global graphs, and further researches how to gain additional insight to facilitate investigations, e.g., by using (Graph) Machine Learning. Learn more about Domain Global Graphs

Potential Topics

• PGX (Learn more about potential topics)
  • Distributed fault tolerance & graph snapshots – exploring various options for enhancing fault tolerance of distributed graph processing systems
  • Extended distributed computations – leveraging an asynchronous depth-first runtime to support a broader scope of computations, such as graph algorithms, machine learning and relational operators
  • Distributed data/graph placement – exploring distributed data/graph placement and partitioning techniques in the presence of concurrent users
  • Distributed graph-based ML– retrieving graph embeddings for ML algorithms from distributed graphs
  • Dynamic data loading for very large graphs – supporting dynamic loading of data that is present in offloaded systems
• Graph-in-DB (Learn more about potential topics)
  • Hybrid execution modes – designing computations to efficiently operate when data is partially on disk and partially in memory
  • Complex analytical queries – exploring latest techniques to autonomously exploit available graph indexes in complex analytical queries
  • ML in the Database – leveraging latest machine learning techniques to improve the performance of various components of Oracle Database
  • Graph algorithm compilation – extending a compiler for a graph-centric domain-specific language
• Domain Global Graphs (Learn more about potential topics)
  • Text to graph conversion – recognizing graph entities and their relationship from unstructured data, employing and improving Seq2Seq, NER, RE, and CR techniques
  • Assistance and automation of global graph investigation workflows – e.g., regarding financial patterns by detecting similar or relevant subgraphs or by automating crime type classification
  • Advanced entity resolution at scale – exploring text embeddings for similarity search, blocking, and graph machine learning techniques
  • Productization of machine learning and graph analytics research – building model serving APIs, training pipelines, and reusable components that cover the whole machine learning operations lifecycle

As an intern, you will participate in the design, implementation, and evaluation of at least one component of the system, and you will give informal and formal presentations on the progress and results obtained during the course of the internship.

The above topics are informative, we offer various topics depending on skills and qualifications of the applicant.

Point of Contact

To apply, please send an email with the required information (see How to Apply above) to Labs-Hiring_ww@oracle.com.

Oracle Autonomous App Platform Project

Cloud-native application platforms simplify development, deployment, and operation of applications in the cloud. For developers, these platforms offer out-of-the-box integration with cloud services such as network management, logging, monitoring, and identity management. For operations, they offer built-in support for platform monitoring as well as some support for automated scaling and failover.

The Autonomous App Platform project aims to develop an app platform built around Oracle's opinionated view of how cloud-native apps should be architected as well as the technology components used to build them. Besides providing seamless integration with various technologies and services, the Autonomous App Platform will offer automated testing, scaling, tuning, and failure detection and recovery to reduce the effort and human labor required to develop and operate help secure, reliable, and performant Java apps.

Potential Topics

• Infrastructure templates for various app patterns
• Tools and analysis for simplifying software composition analysis
• App incident detection, management, and remediation
• Automated testing
• App hardening

Point of Contact

To apply, please send an email with the required information (see How to Apply above) to Labs-Hiring_ww@oracle.com.

Graal Cloud Native

Graal Cloud Native (GCN) is an Open Source developer-centered platform built on the Java ecosystem to dramatically improve developer productivity when building applications and microservices leveraging Oracle Cloud. GCN accomplishes that by providing automation in writing applications, managing configurations, and allowing developers to rapidly build/test/deploy/debug their application from their IDE (Visual Studio Code). GCN contains GraalVM Native Image, the Micronaut framework, GraalVM Tools for Java and Micronaut VS Code IDE Extensions, documentation, hands-on tutorials, and Luna Labs based hands-on experience.

Possible Research Areas

• Deep integration with Multiple Clouds
• IDE-based tools for improving developer productivity
• Abstractions over Services available on Multiple Clouds
• Tools for dramatically improving tracing, logging, and debugging in Cloud Computing

Point of Contact

To apply, please send an email with the required information (see How to Apply above) to gcn-internships_us_grp@oracle.com.

ML for Security Applications – KeyBridge

Our mission is increasing the security posture of both Oracle cloud customers and the Oracle's teams operating the cloud infrastructure.

Our research vision is of automated detection and mitigation of security events, allowing security experts to scale their efforts to large infrastructures such as the cloud. In the process, we working on data preparation and data exploration, statistical analysis and anomaly detection, alert generation and presentation of results.

Our analytics toolbelt includes techniques from data exploration, statistical analysis and deep learning. Working with application logs means that representation learning, machine learning for code and embedding techniques are fundamental research topics for us. So is explainable machine learning. For the scalable processing of the high volumes of logs we get, we are developing a machine learning pipeline framework that operates on top of various state-of-the-art libraries and follows a modular design.

Our team is a motley crew of researchers with diverse backgrounds in data analytics, machine learning, network and system design, and software development. We share a passion to develop reliable, innovative solutions to security problems with practical relevance. We place high value on a collaboration spirit, an inquisitive mindset and a drive to deliver high quality.

Learn more about KeyBridge

If you share our interests and values, we would be happy to welcome you in the family. Some ideas of directions in which an internship can go:

• Explorative: investigation of what machine learning techniques are applicable to a particular security problem.
• Research in ML: development of machine learning solutions for log encoding, source code generation, information retrieval, anomaly detection, building behavior profiles.
• Research in HCI: investigation of approaches towards data presentation for operational teams.
• Software development: development of scalable machine learning pipelines

Point of Contact

To apply, please send an email with the required information (see How to Apply above) to olabs-keybridge-hiring_ww@oracle.com.

Intelligent Application Security

The Intelligent Application Security team at Oracle Labs works on innovative projects in the application security space spanning areas like program analysis, machine learning, software composition analysis, malware detection, and runtime protection. The team is based in Brisbane, Australia with a few remote members based in in Austria and South Korea. Candidates who hold work rights in any of Australia, Austria or South Korea are welcome to apply. Internships in the IAS team offer exciting opportunities to those who are passionate about improving application security. The ideal candidate will relish the challenge of developing techniques that can be applied to cloud-scale applications.

Our internships cater to a wide variety of students studying computer science or software engineering including those who are in the final year of their undergraduate degree or are undertaking research at the master's or PhD level.

Learn more about RASPunzel

Learn more about OSInt (Open-Source Intelligence)

Learn more about Parfait Labs

Potential Topics

• Vulnerability analysis of binary code
• Combining machine learning and program analysis to improve vulnerability detection
• Synthesis of security monitors
• Automated program repair
• Analysis of infrastructure as code including securing container orchestration frameworks and CI/CD pipelines
• Automated malware analysis and threat intelligence
• Supply chain attack detection
• Fuzzing

Point of Contact

To apply, please send an email with the required information (see How to Apply above) to ias-internships-au_au_grp@oracle.com.

Machine Learning Research Group

The mission of the Machine Learning Research Group (MLRG) is to scale Machine Learning (ML) across Oracle by evangelizing ML throughout Oracle, collaborating with product groups to develop ML-based solutions to improve their products and services, developing ML tools and systems to help them implement ML solutions on their own, and doing fundamental ML research to better understand what will be needed by product groups in the future.

The MLRG is composed of researchers, data scientists, and engineers that collaborate closely with other Oracle Labs research groups, and with product groups and business units across the entire company. These collaborations run the gamut from fundamental ML research around fairness, privacy, and robustness in ML systems, training and evaluating large-scale contextual embeddings for natural language processing, and building tools for ML.

Learn more about the project

Potential Topics

• Building more robust intent classification systems by augmenting data
• Improving the performance of multi-label image classification systems through failure analysis
• Building classifiers to segment complicated email bodies and support texts more accurately
• Exploring new techniques for building large language models with less data, faster
• Investigating the uses of differential privacy and homomorphic encryption in federated learning systems

Point of Contact

To apply, please send an email with the required information (see How to Apply above) to mlrg-internships_ww@oracle.com.