Graphics Software Engineer, Greater London

TN United Kingdom
Greater London
2 months ago
Applications closed

Related Jobs

View all jobs

Machine Learning Engineer (3D)

Computer Vision internship - West London

Senior Electronics Engineer

Lead Data Analyst

Social network you want to login/join with:

Graphics Software Engineer, Greater LondonClient:

Apple

Location:

Greater London, United Kingdom

Job Category:

Other

EU work permit required:

Yes

Job Reference:

30c4b4b9155c

Job Views:

7

Posted:

03.03.2025

Job Description:

Summary:
Imagine what you could do here. At Apple, new ideas have a way of becoming extraordinary products, services, and customer experiences very quickly. Bring passion and dedication to your job and there's no telling what you could accomplish. Dynamic, smart people and inspiring, innovative technologies are the norm here. The people who work here have reinvented entire industries with all Apple Hardware products. The same passion for innovation that goes into our products also applies to our practices strengthening our commitment to leave the world better than we found it. We’re looking for those with talent and ambition to innovate the way we design Apple silicon graphics processors, to provide the next technological leap and improve customer experiences in areas like real-time graphics, VR/AR, parallel computing and deep learning and welcome you to work among the industry’s best. As a Graphics Software Engineer at our GPU UK Design Centre, you are responsible for developing GPU workloads, automated flows and tools to support the verification process of our GPU designs. You will work alongside teams of architects, hardware, software and verification engineers to ensure the functionality, performance and power of our GPU designs can be efficiently and effectively verified.

Key Qualifications:
Excellent communications skills. Self-motivated and organised.
Excellent C/C++ programming and problem solving skills.
Strong understanding of rendering and/or concurrent programming algorithms.
Experience with one or more GPU APIs (Metal, DX12, Vulkan, CUDA, OpenGL and/or OpenCL).
Experience with scripting languages, such as Python.
Familiar with one or more GPU or CPU hardware architectures.
Architecture validation and/or design verification knowledge desirable.
GPU/CPU performance analysis experience desirable.
Experience with GPU API capture and analysis tools desirable.

Description:
In this role, you will:

  1. Define, author and debug GPU architecture functional, performance and power test suites.
  2. Support GPU model, hardware design, and hardware verification teams pre / post silicon.
  3. Lead the design and implementation of GPU verification tools and APIs.
  4. Create production quality automated flows for graphics core verification.
  5. Provide insight into how real-world workloads could stress the GPU architecture and benefit from new features.
  6. Challenge architectural design decisions. Propose refinements based on issues found.
  7. Support GPU software teams during driver bring-up.

Additional Requirements:
Some international travel will be required.

#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.

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!

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.

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.