Research Scientist, 3D ML, AI & Computer Vision (PhD)

Meta
London
1 week ago
Applications closed

Related Jobs

View all jobs

Machine Learning Research Scientist - PhD, NLP, LLM

Clinical Data Scientist

Data Scientist

Data Scientist - (Senior AI/ML Engineer)

Genomic Data Scientist

Data Scientist - £40k - ID40553

Meta Reality Labs Research (RL Research) brings together a world-class R&D team of researchers, developers, and engineers with the shared goal of developing AI and AR/VR technology across the spectrum. The Surreal Spatial AI group is seeking exceptional research scientists to build machine perception technology allowing AI agents and systems to perceive and understand the 3D world around them. The aim of this role is to develop, advance and integrate ML and computer vision models and SW systems for advanced, real-time machine perception for egocentric devices such as Meta's Project Aria, including 3D environment and object reconstruction, semantic understanding, estimation and understanding of user motion, actions and activities, and learning of predictive, causal world models for embodied AI agents and egocentric devices.

Research Scientist, 3D ML, AI & Computer Vision (PhD) Responsibilities

  • Research, develop and prototype state of the art ML and software technology in the domains of 3D environment and object reconstruction, semantic understanding, estimation and understanding of user motion and more.
  • Build/integrate real-time prototypes for advanced, real-time 3D machine perception systems as part of a fast-moving research and research engineering team.
  • Collaborate with team members throughout the lifetime of a project, from prototyping to deployed products.
  • Deliver research results that have a direct impact on Meta and Meta's AI-enabled products.

Minimum Qualifications

  • Currently has or is in the process of obtaining a PhD in the field of computer vision, Machine Learning / Artificial Intelligence, robotics or equivalent.
  • Track-record of state-of-the-art publications in the field and at top conferences in the field.
  • Experienced in advanced 3D computer vision and ML, thorough understanding of 3D geometry fundamentals as well as state of the art ML/AI models incorporate that.
  • Mathematical background and understanding of numerical optimization, linear algebra, probabilistic estimation and 3D geometry.
  • 5+ years C++ experience with a mastery of modern C++ features.
  • 3+ years python experience, including appropriate ML frameworks.
  • Interpersonal experience: cross-group and cross-functional collaboration.
  • Must obtain work authorization in the country of employment at the time of hire and maintain ongoing work authorization during employment.

Preferred Qualifications

  • Experience working on real-time, high-performance systems in robotics, AR/VR, or other areas.
  • Experience working in a Linux environment.
  • Broad understanding of the state of the art of ML and AI model architectures and training paradigms.
  • 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 CVPR, ECCV/ICCV, ICCP, 3DV, BMVC, or SIGGRAPH.
  • Demonstrated software engineer experience via an internship, work experience, coding competitions, or widely used contributions in open source repositories (e.g. GitHub).

#J-18808-Ljbffr

Get the latest insights and jobs direct. Sign up for our newsletter.

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

Industry Insights

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

Quantum-Enhanced Machine Learning—Propelling AI into the Next Frontier

Machine learning (ML) has revolutionised how we interpret data, build predictive models, and create intelligent applications. From recommendation engines and self-driving cars to advanced genomics and natural language processing, ML solutions are integral to nearly every corner of modern life. However, as data complexity and model size continue to skyrocket, the computational demands placed on ML systems grow in tandem—often pushing even high-performance classical computers to their limits. In recent years, quantum computing has emerged as a tantalising solution to these challenges. Unlike traditional digital systems, quantum computers exploit quantum mechanics—superposition and entanglement—to process information in ways that defy conventional logic. As these machines mature, they promise exponential speed-ups for certain tasks, potentially reshaping how we approach AI and data-intensive challenges. What does this mean for machine learning? Enter quantum-enhanced ML, a new frontier where quantum processors and classical ML frameworks unite to accelerate model training, tackle high-dimensional data, and solve complex optimisation tasks more efficiently. In this article, we will: Unpack the current state of machine learning, highlighting key bottlenecks. Provide a concise overview of quantum computing—why it’s radical and how it differs from classical technology. Examine potential breakthroughs in quantum-enhanced ML, including real-world use cases and technical approaches. Explore the roles and skill sets that will define this quantum-AI era, with guidance on how to prepare. Discuss the roadblocks (like hardware maturity and ethical concerns) and how they might be addressed in the years to come. If you’re a machine learning engineer, data scientist, or simply an AI enthusiast fascinated by the next wave of computational innovation, read on—quantum computing could become an integral part of your future toolkit, opening up job opportunities and reimagining what ML can achieve.

Machine Learning Jobs at Newly Funded UK Start-ups: Q3 2025 Investment Tracker

Machine learning (ML) has become the beating heart of modern tech innovation, powering breakthroughs in healthcare, finance, cybersecurity, robotics, and more. Across the United Kingdom, this surge in ML-driven solutions is fueling the success of countless start-ups—and spurring demand for talented machine learning engineers, data scientists, and related professionals. If you’re eager to join a high-growth ML company or simply want to keep tabs on the latest trends, this Q3 2025 Investment Tracker will guide you through the newly funded UK start-ups pushing the boundaries of ML. In this article, we’ll highlight key developments from Q3 2025, delve into the most promising newly funded ventures, and shed light on the machine learning roles they’re urgently seeking to fill. Plus, we’ll show you how to connect with these employers via MachineLearningJobs.co.uk, a dedicated platform for ML job seekers. Let’s dive in!

Portfolio Projects That Get You Hired for Machine Learning Jobs (With Real GitHub Examples)

In today’s data-driven landscape, the field of machine learning (ML) is one of the most sought-after career paths. From startups to multinational enterprises, organisations are on the lookout for professionals who can develop and deploy ML models that drive impactful decisions. Whether you’re an aspiring data scientist, a seasoned researcher, or a machine learning engineer, one element can truly make your CV shine: a compelling portfolio. While your CV and cover letter detail your educational background and professional experiences, a portfolio reveals your practical know-how. The code you share, the projects you build, and your problem-solving process all help prospective employers ascertain if you’re the right fit for their team. But what kinds of portfolio projects stand out, and how can you showcase them effectively? This article provides the answers. We’ll look at: Why a machine learning portfolio is critical for impressing recruiters. How to select appropriate ML projects for your target roles. Inspirational GitHub examples that exemplify strong project structure and presentation. Tangible project ideas you can start immediately, from predictive modelling to computer vision. Best practices for showcasing your work on GitHub, personal websites, and beyond. Finally, we’ll share how you can leverage these projects to unlock opportunities—plus a handy link to upload your CV on Machine Learning Jobs when you’re ready to apply. Get ready to build a portfolio that underscores your skill set and positions you for the ML role you’ve been dreaming of!