Research Scientist, Machine Learning (PhD)

Meta
City of London
2 months ago
Applications closed

Related Jobs

View all jobs

Research Scientist, Machine Learning

Research Scientist (Machine Learning), London London

Research Scientist (Quantum Chemistry and Machine Learning), London London

Senior Research Scientist: Data Science and Machine Learning

Research Scientist (Machine Learning), London

Senior Research Scientist: Data Science and Machine Learning AIP

Overview

Summary:

Meta is at the forefront of a transformative change in its business and technology, and our research teams are driving this evolution. As a Research Scientist at Meta, you will have the opportunity to work on crucial projects and initiatives that have never been done before, helping to advance the way people connect around the world. You will be responsible for proactively identifying and driving changes as needed for your assigned codebase, product area, and/or systems, building strong cross-functional partnerships, and suggesting, collecting, and synthesizing requirements to create effective feature roadmaps. You will work alongside the world’s leading engineers and researchers to solve some of the most exciting and massive social data and prediction problems that exist. As a member of our team, you will have the opportunity to apply your technical expertise to drive innovation and solve complex problems. Your experience with frameworks such as PyTorch, TensorFlow, or equivalent, as well as your ability to translate insights into business recommendations, will be valuable assets in this role. We are looking for individuals who are passionate about building and shipping high-quality work, achieving high reliability, and working independently without guidance. This is a Software Engineering role, and core responsibilities include coding and applied engineering work. You will be expected to write high-quality, efficient, and maintainable code, and contribute to the development of innovative software solutions. In this role, you will have the opportunity to work on cutting-edge projects, collaborate with cross-functional teams, and contribute to the development of innovative solutions that push the boundaries of scalable computing. We offer a dynamic and innovative work environment that encourages creativity and experimentation, as well as opportunities for professional growth and development. If you are passionate about driving innovation and advancing the way people connect, we encourage you to apply for this exciting opportunity.

Required Skills

Research Scientist, Machine Learning (PhD) Responsibilities:

  • Proactively identify and drive changes as needed for your assigned codebase, product area and/or systems
  • Build strong cross functional partnerships and code deliverables
  • Suggest, collect and synthesize requirements and create effective feature roadmaps
  • Perform specific responsibilities which vary by team
Minimum Qualifications
  1. Currently has, or is in the process of obtaining, a PhD degree or completing a postdoctoral assignment in the field of Computer Science, Computer Engineering or relevant technical field. Degree must be completed prior to joining Meta
  2. Currently has, or is in the process of obtaining a Bachelor\'s degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience. Degree must be completed prior to joining Meta
  3. Relevant experience using frameworks such as PyTorch, TensorFlow or equivalent
  4. Proven experience to translate insights into business recommendations
  5. Experience building and shipping high quality work and achieving high reliability
  6. Experience in systems software or algorithms
  7. Experience programming in a relevant language
  8. Experience identifying, designing and completing medium to large features independently, without guidance
  9. Must obtain work authorization in country of employment at the time of hire, and maintain ongoing work authorization during employment
Preferred Qualifications
  1. Demonstrated software engineer experience via an internship, work experience, coding competitions, or used contributions in open source repositories (e.g. GitHub)
  2. Research and/or hands-on experience in one or more of the following areas: Machine Learning, NLP, Recommendation Systems, Pattern Recognition, Data Mining, Computer Vision, Artificial Intelligence or other relevant fields
  3. Experience with programming languages such as Python, R, MATLAB
  4. Proven track record of achieving results as demonstrated by grants, fellowships, patents, as well as first-authored publications at workshops or conferences such as KDD, NeurIPS, ICML, WWW, ACL, ICLR, CVPR or similar
  5. Experience solving complex problems and comparing alternative solutions, tradeoffs, and different perspectives to determine a path forward
  6. Interpersonal experience working and communicating cross functionally in a team environment
  7. Exposure to architectural patterns of large scale software applications

Industry: Internet


#J-18808-Ljbffr

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.