Student Veterans of America Jobs

Welcome to SVA’s jobs portal, your one-stop shop for finding the most up to date source of employment opportunities. We have partnered with the National Labor Exchange to provide you this information. You may be looking for part-time employment to supplement your income while you are in school. You might be looking for an internship to add experience to your resume. And you may be completing your training ready to start a new career. This site has all of those types of jobs.

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Job Information

Intel Graduate Research Intern in Madrid, Spain

Job Description

AI Safety research intern is responsible for performing research, develop, implement and test new AI Safety methods (detection and mitigation), using cyber safety methods and tools on workloads in research and/or production environments.

  • Takes initiative in complex, multidisciplinary performance research projects. The researcher duty extends to building internal tools to help find and diagnose vulnerabilities for a wide range of use cases automatically and seamlessly.

  • Present extensive knowledge of AI Safety methods, Large Language Models and Multimodal models, and more. Demonstrates capabilities in learning and researching new domains, requiring the ability to navigate around ambiguities and obstacles to independently solve complex performance issues.

  • Shares expert insights and new learnings and contributes intellectual property to internal, external and open source communities. Uses software development processes and methodologies while building complex software systems.

  • Ensures that research output does not stop at just finding the specific optimizations for a client, but also includes recommendations for making them generic and widely used for additional clients.

  • Share knowledge, and educate other team members and coworkers.

  • Demonstrate effective communication with executives and productive collaboration with peer researchers.

Intelligent Systems Research Lab (ISR) is focused on multi-disciplinary research spanning ethnography, design, HCI, and AI to create Human/AI collaborative systems that can amplify human potential and create sustainable and transparent AI solutions. We utilize multi-modal signals (e.g. vision, audio, speech, language, RF) and AI to infer and predict human state and actions and enable physically situated dialog and interactions. We conduct this research in the context of vertical domains including manufacturing, education, enterprise, and assistive computing for people with disabilities.Given the fast progress in AI and large language models and its widespread adoption in the academia, industry and other areas, ensuring the safety of these models is extremely important. ISR is looking for an intern passionate in the area of AI Safety.

The work will involve building a human-in-the-loop red-teaming framework for model evaluation and understanding. The work will also include exploration of how to make these model outputs more understandable for an end-user or developer trying to incorporate it for a downstream task. In this context, the intern will be able to explore techniques that will enable human experts to interact with the model evaluation framework, and also come up with novel red-teaming techniques and mitigation of harms in LLMs such as toxic responses/bias. This research will enable the Responsible AI processes within Intel and also the publication of the findings in both internal and external venues.

Qualifications

  • Enrolled in a PhD program in artificial intelligence, machine learning, computer science, statistics, electrical engineering, or a relevant engineering/science discipline

  • 1 year of research experience in machine learning, deep learning, or natural language processing

  • 1 year of experience in the development of algorithms in high-level languages such as Python and C++ Strong verbal and written communication skills

  • Prior research in Vision-Language Systems and RAG pipelines -Prior research in Large Language Models and understanding of optimization and model tuning techniques

Preferred Qualifications:

  • Publications in top-tier conferences and journals in machine learning related fields (NeurIPS, ICML, AAAI, ACL, etc.)

  • 2+ year of experience with deep learning frameworks (TensorFlow, PyTorch, MXNet, etc.)

Requirements listed would be obtained through a combination of industry relevant job experience, internship experiences and or schoolwork/classes/research.

Inside this Business Group

Intel Labs is the company's world-class, industry leading research organization, responsible for driving Intel's technology pipeline and creating new opportunities. The mission of Intel Labs is to deliver breakthrough technologies to fuel Intel's growth. This includes identifying and exploring compelling new technologies and high risk opportunities ahead of business unit investment and demonstrating first-to-market technologies and innovative new usages for computing technology. Intel Labs engages the leading thinkers in academia and industry in addition to partnering closely with Intel business units.

Posting Statement

All qualified applicants will receive consideration for employment without regard to race, color, religion, religious creed, sex, national origin, ancestry, age, physical or mental disability, medical condition, genetic information, military and veteran status, marital status, pregnancy, gender, gender expression, gender identity, sexual orientation, or any other characteristic protected by local law, regulation, or ordinance.

Benefits

We offer a total compensation package that ranks among the best in the industry. It consists of competitive pay, stock, bonuses, as well as, benefit programs which include health, retirement, and vacation. Find more information about all of our Amazing Benefits here. (https://jobs.intel.com/en/benefits)

Working Model

This role is available as a fully home-based and generally would require you to attend Intel sites only occasionally based on business need. This role may also be available as our hybrid work model which allows employees to split their time between working on-site at their assigned Intel site and off-site. In certain circumstances the work model may change to accommodate business needs.

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