Machine Learning Engineer (3D Gaussian Splatting & NeRF)

M-XR
London
4 months ago
Applications closed

Related Jobs

View all jobs

Machine Learning Engineer( Real time Data Science Applications)

Machine Learning Engineer

Machine Learning Engineer

Machine Learning Engineer

Machine Learning Engineer

Machine Learning Engineer

Summary


M-XR is a deep tech startup with a mission to make the 3D digital world look real; whether that be the graphics in a computer game, the CGI in a movie, or a product line photoshoot. We are building a solution that empowers 3D creators and enables the creation of productions at a speed, scale and quality not found anywhere else in the industry. Over the past three years we’ve developed foundational technology capable of capturing real world objects and accurately predicting their material properties, enabling the creation of ultra-realistic production-ready digital copies.


Curiosity and creativity are at the heart of M-XR. We feel strong that asking questions and looking at problems from new perspectives across departments is key to pushing the envelope for what is possible! We are looking for skilled individuals who share this passionate curiosity, question the norm, and have the willingness to explore something brand new. If you are an engineer or developer that shares this passion about shaping the future of 3D we would love to hear from you.


Description of work to be performed


As a Machine Learning Engineer at M-XR specializing in Neural Radiance Fields (NeRF) and 3D Gaussian Splatting (3DGS), you will play a pivotal role in advancing our capabilities. Your focus will be on implementing cutting-edge computer vision algorithms for NeRF/3DGS and exploring ways to enhance these technologies by integrating segmentation, language embeddings (e.g., CLIP), and other advancements in 3D machine learning.


Leveraging M-XR’s proprietary dataset—the highest-quality ultra-realistic 3D dataset of its kind—you’ll tackle innovative projects that push the boundaries of what’s possible in machine learning and 3D. Key initiatives include 3D asset relighting and extracting ultra-realistic material properties directly from NeRFs/3DGS. A core part of your role will involve adapting, implementing, and enhancing open-source research and models such as SAM, Stable Diffusion, CLIP, DINO, NeRF, and 3DGS to create solutions tailored to our unique use cases.


Your contributions will directly set new standards for realism and quality in 3D content creation, with applications in major film and game productions. This role offers an exciting opportunity to tackle complex challenges, develop groundbreaking technologies, and witness the tangible impact of your work on the future of the entertainment industry.


Ideal Candidate


•Creative and innovative thinker

•Resourceful and effective problem-solver

•Clear, articulate, and proactive communicator

•Strong user-focused mindset

  • Collaborative and supportive team player


Requirements


•Proficiency with a major industry ML framework (e.g., PyTorch, JAX, TensorFlow)

•Expertise in writing production-quality Python code

•Experience with CUDA programming

•Familiarity with Git and version control systems

•Strong understanding of computer graphics principles

•Hands-on experience with 3D Gaussian Splatting, either through contributions to open-source NeRF/3DGS repositories or solving relevant problems in the field

•Practical experience in training and fine-tuning diffusion models (a plus)

•Knowledge of C++ (an advantage)


Please ensure your CV is attached.


Best,

M-XR


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.

Jobs

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!

Jobs Careers

Machine Learning Job Interview Warm‑Up: 30 Real Coding & System‑Design Questions

Machine learning is fuelling innovation across every industry, from healthcare to retail to financial services. As organisations look to harness large datasets and predictive algorithms to gain competitive advantages, the demand for skilled ML professionals continues to soar. Whether you’re aiming for a machine learning engineer role or a research scientist position, strong interview performance can open doors to dynamic projects and fulfilling careers. However, machine learning interviews differ from standard software engineering ones. Beyond coding proficiency, you’ll be tested on algorithms, mathematics, data manipulation, and applied problem-solving skills. Employers also expect you to discuss how to deploy models in production and maintain them effectively—touching on MLOps or advanced system design for scaling model inferences. In this guide, we’ve compiled 30 real coding & system‑design questions you might face in a machine learning job interview. From linear regression to distributed training strategies, these questions aim to test your depth of knowledge and practical know‑how. And if you’re ready to find your next ML opportunity in the UK, head to www.machinelearningjobs.co.uk—a prime location for the latest machine learning vacancies. Let’s dive in and gear up for success in your forthcoming interviews.

Jobs

Negotiating Your Machine Learning Job Offer: Equity, Bonuses & Perks Explained

How to Secure a Compensation Package That Matches Your Technical Mastery and Strategic Influence in the UK’s ML Landscape Machine learning (ML) has rapidly shifted from an emerging discipline to a mission-critical function in modern enterprises. From optimising e-commerce recommendations to powering autonomous vehicles and driving innovation in healthcare, ML experts hold the keys to transformative outcomes. As a mid‑senior professional in this field, you’re not only crafting sophisticated algorithms; you’re often guiding strategic decisions about data pipelines, model deployment, and product direction. With such a powerful impact on business results, companies across the UK are going beyond standard salary structures to attract top ML talent. Negotiating a compensation package that truly reflects your value means looking beyond the numbers on your monthly payslip. In addition to a competitive base salary, you could be securing equity, performance-based bonuses, and perks that support your ongoing research, development, and growth. However, many mid‑senior ML professionals leave these additional benefits on the table—either because they’re unsure how to negotiate them or they simply underestimate their long-term worth. This guide explores every critical aspect of negotiating a machine learning job offer. Whether you’re joining an AI-focused start-up or a major tech player expanding its ML capabilities, understanding equity structures, bonus schemes, and strategic perks will help you lock in a package that matches your technical expertise and strategic influence. Let’s dive in.

🍪 We use cookies to enhance your browsing experience on our website. By continuing to use this site, you consent to the use of cookies as described in our Cookie Policy. You can review our Cookie Policy for more information.