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- Tasks: Dive into cutting-edge AI research and develop innovative NLP algorithms.
- Company: Join Meta, a leader in advancing artificial intelligence technologies.
- Benefits: Flexible internship duration with various start dates and opportunities for impactful work.
- Why this job: Make significant contributions to AI while collaborating with top researchers in a dynamic environment.
- Qualifications: Pursuing a PhD in relevant fields with experience in Python and deep learning frameworks.
- Other info: Ideal for those looking to return to academia after the internship.
We are committed to advancing the field of artificial intelligence by making fundamental advances in technologies to help interact with and understand our world. We are seeking individuals passionate in areas such as deep learning, computer vision, audio and speech processing, natural language processing, machine learning, reinforcement learning, computational statistics, and applied mathematics. Our interns have an opportunity to make core algorithmic advances and apply their ideas at an unprecedented scale. Our internships are twelve (12) to sixteen (16), or twenty-four (24) weeks long and we have various start dates throughout the year. Research Scientist Intern, Large Language Models (PhD) Responsibilities Perform research to advance the science and technology of intelligent machines. Develop novel and accurate NLP algorithms and systems, leveraging Deep Learning and Machine Learning on big data resources. Analyze and improve efficiency, scalability, and stability of various deployed systems. Collaborate with researchers and cross-functional partners including communicating research plans, progress, and results. Publish research results and contribute to research that can be applied to Meta product development. Company: Meta Qualifications: Minimum Qualifications: Currently has or is in the process of obtaining a PhD degree in Computer Science, Artificial Intelligence, Natural Language Processing, Speech Recognition, or relevant technical field. Must obtain work authorization in the country of employment at the time of hire and maintain ongoing work authorization during employment. Experience with Python, C++, C, Java or other related languages. Experience with deep learning frameworks such as Pytorch or Tensorflow. Experience building systems based on machine learning, deep learning methods, or natural language processing. Preferred Qualifications: Intent to return to a degree program after the completion of the internship/co-op. Proven track record of achieving significant results as demonstrated by grants, fellowships, patents, as well as first-authored publications at leading workshops or conferences such as NeurIPS, ICLR, ICML, ACL, NAACL, EMNLP, or similar. Experience with ML areas such as Natural Language Processing, Speech, Multimodal Reasoning & Retrieval. Experience manipulating and analyzing complex, high-volume, high-dimensionality data from varying sources. Experience with training deep neural networks for key NLP tasks. Experience with interpreting deep neural networks mechanistically, correlating their observable behavior with properties of model parameters and activations. Demonstrated software engineer experience via an internship, work experience, coding competitions, or widely used contributions in open source repositories (e.g. GitHub). Experience working and communicating cross functionally in a team environment. Educational level: Ph. D. #J-18808-Ljbffr
Research Scientist Intern, Large Language Models (PhD) employer: NLP PEOPLE
Contact Detail:
NLP PEOPLE Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Research Scientist Intern, Large Language Models (PhD)
✨Tip Number 1
Make sure to showcase your passion for artificial intelligence and deep learning in your conversations. When networking or during interviews, share specific projects or research that highlight your enthusiasm and expertise in these areas.
✨Tip Number 2
Familiarize yourself with the latest advancements in NLP and deep learning frameworks like PyTorch and TensorFlow. Being able to discuss recent papers or breakthroughs can demonstrate your commitment to staying updated in the field.
✨Tip Number 3
Engage with the research community by attending workshops or conferences related to machine learning and natural language processing. This not only helps you learn but also provides networking opportunities that could lead to referrals or insights about the internship.
✨Tip Number 4
Collaborate on open-source projects or contribute to GitHub repositories related to AI and NLP. This hands-on experience will not only enhance your skills but also serve as a strong talking point during interviews, showcasing your practical knowledge.
We think you need these skills to ace Research Scientist Intern, Large Language Models (PhD)
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your relevant experience in deep learning, natural language processing, and any programming languages mentioned in the job description. Include specific projects or research that demonstrate your skills.
Craft a Strong Cover Letter: In your cover letter, express your passion for artificial intelligence and how your background aligns with the responsibilities of the Research Scientist Intern position. Mention any significant results from your previous work, such as publications or projects.
Showcase Your Research Experience: Detail your research experience, especially any work related to NLP algorithms or machine learning systems. Highlight any collaborations with cross-functional teams and your contributions to published research.
Prepare for Technical Questions: Be ready to discuss your technical skills and experiences during the interview process. Prepare to explain your understanding of deep learning frameworks like Pytorch or Tensorflow and how you've applied them in your projects.
How to prepare for a job interview at NLP PEOPLE
✨Show Your Passion for AI
Make sure to express your enthusiasm for artificial intelligence and its applications. Discuss specific areas of interest, such as deep learning or natural language processing, and how they align with the company's mission.
✨Demonstrate Technical Proficiency
Be prepared to discuss your experience with programming languages like Python, C++, or Java, as well as deep learning frameworks such as PyTorch or TensorFlow. Highlight any projects where you've applied these skills, especially in NLP or machine learning.
✨Prepare for Research Discussions
Since the role involves performing research, be ready to talk about your previous research experiences. Discuss any significant results you've achieved, publications, or contributions to conferences that showcase your expertise in the field.
✨Emphasize Collaboration Skills
Collaboration is key in this role, so share examples of how you've worked effectively in teams. Highlight your ability to communicate research plans and results clearly, and how you’ve engaged with cross-functional partners in past projects.
