Software Engineer Intern, Machine Learning (PhD)

Meta
London
1 year ago
Applications closed

Related Jobs

View all jobs

Machine Learning Researcher

Data Scientist Senior Consultant

Software Engineer (AI & Machine Learning)

Software Engineer - Large Language Models

Software Engineer - Large Language Models

Software Engineer (AI & Machine Learning)

Summary: Meta is embarking on the most transformative change to its business and technology in company history, and our Machine Learning teams are at the forefront of this evolution. By taking on crucial projects and initiatives that have never been done before, you have an opportunity to help advance the way people connect around the world.In order to meet the demands of our scale, we approach machine learning challenges from a system engineering standpoint, pushing the boundaries of scalable computing and tying together numerous complex platforms to build models that leverage trillions of actions. Our research and production implementations leverage many of the innovations being generated from Meta's research in Distributed Computing, Artificial Intelligence and Databases, and run on the same hardware and network specifications that are being open sourced through the Open Compute project.As a PhD intern at Meta, you will help build machine learning systems and models behind Meta's products, create web applications that reach millions of people, build high volume servers and be a part of a team that's working to help connect people around the globe.As part of our hiring process, PhD interns are matched to a relevant team based on their experience and interests.This internship has a minimum twelve (12) week duration with 2025 start dates only. Required Skills: Software Engineer Intern, Machine Learning (PhD) Responsibilities: - Develop highly scalable classifiers and tools leveraging machine learning, regression, and rules-based models - Suggest, collect and synthesize requirements and create effective feature roadmap - Code deliverables in tandem with the engineering team - Adapt standard machine learning methods to best exploit modern parallel environments (e.g. distributed clusters, multicore SMP, and GPU) - Perform specific responsibilities which vary by team Minimum Qualifications: Minimum Qualifications: - Currently has, or is in the process of obtaining, a PhD in Computer Science, Computer Vision, Machine Learning, or related field - Research and/or work experience in a relevant field, such as machine learning, deep learning, reinforcement learning, NLP, recommendation systems, pattern recognition, signal processing, data mining, artificial intelligence, or computer vision - Experience in systems software or algorithms - Experience coding in Java, C/C++, Perl, PHP, or Python - Interpersonal experience: cross-group and cross-culture collaboration - Must obtain work authorization in country of employment at the time of hire, and maintain ongoing work authorization during employment Preferred Qualifications: Preferred Qualifications: - Intent to return to degree-program after the completion of the internship/co-op - Demonstrated software engineer experience via an internship, work experience, coding competitions, or PhD papers - Proven track record of achieving significant results as demonstrated by grants, fellowships, patents, as well as first-authored publications at leading workshops or conferences - Demonstrated creativity and quick problem solving capabilities - Experience with Hadoop/Hbase/Pig or Mapreduce/Sawzall/Bigtable Industry: Internet

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

By subscribing, you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

How Many AI Tools Do You Need to Know to Get an AI Job?

If you are job hunting in AI right now it can feel like you are drowning in tools. Every week there is a new framework, a new “must-learn” platform or a new productivity app that everyone on LinkedIn seems to be using. The result is predictable: job seekers panic-learn a long list of tools without actually getting better at delivering outcomes. Here is the truth most hiring managers will quietly agree with. They do not hire you because you know 27 tools. They hire you because you can solve a problem, communicate trade-offs, ship something reliable and improve it with feedback. Tools matter, but only in service of outcomes. So how many AI tools do you actually need to know? For most AI job seekers: fewer than you think. You need a tight core toolkit plus a role-specific layer. Everything else is optional. This guide breaks it down clearly, gives you a simple framework to choose what to learn and shows you how to present your toolset on your CV, portfolio and interviews.

What Hiring Managers Look for First in AI Job Applications (UK Guide)

Hiring managers do not start by reading your CV line-by-line. They scan for signals. In AI roles especially, they are looking for proof that you can ship, learn fast, communicate clearly & work safely with data and systems. The best applications make those signals obvious in the first 10–20 seconds. This guide breaks down what hiring managers typically look for first in AI applications in the UK market, how to present it on your CV, LinkedIn & portfolio, and the most common reasons strong candidates get overlooked. Use it as a checklist to tighten your application before you click apply.

The Skills Gap in AI Jobs: What Universities Aren’t Teaching

Artificial intelligence is no longer a future concept. It is already reshaping how businesses operate, how decisions are made, and how entire industries compete. From finance and healthcare to retail, manufacturing, defence, and climate science, AI is embedded in critical systems across the UK economy. Yet despite unprecedented demand for AI talent, employers continue to report severe recruitment challenges. Vacancies remain open for months. Salaries rise year on year. Candidates with impressive academic credentials often fail technical interviews. At the heart of this disconnect lies a growing and uncomfortable truth: Universities are not fully preparing graduates for real-world AI jobs. This article explores the AI skills gap in depth—what is missing from many university programmes, why the gap persists, what employers actually want, and how jobseekers can bridge the divide to build a successful career in artificial intelligence.