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For Companies At a Glance
- Tasks: Design high-performance algorithms and optimise GPU processing for innovative applications.
- Company: Join a cutting-edge tech company with a collaborative and supportive culture.
- Benefits: Enjoy a healthy work-life balance, competitive salary, and opportunities for professional growth.
- Why this job: Make an impact in entertainment and science while working with the latest GPU technologies.
- Qualifications: Strong C++ skills and experience with CUDA required; real-time systems experience is a plus.
- Other info: Dynamic environment with close collaboration with machine learning experts.
The predicted salary is between 36000 - 60000 £ per year.
We are seeking a skilled C++ Engineer with strong GPU acceleration expertise to work on cutting-edge, high-performance systems used across entertainment, engineering, and scientific applications. This role focuses on maximising GPU-based processing performance, including real-time data handling, image processing, and machine learning workloads. You will join a collaborative software engineering team and work closely with machine learning and research specialists in an environment that values technical excellence, innovation, and a healthy work–life balance.
Key Responsibilities
- Design and implement high-performance algorithms using CUDA
- Manage host–device interactions, including memory management, data transfer optimisation, and multi-GPU support
- Deploy and optimise machine learning models using TensorRT within C++ applications
- Profile and optimise GPU workloads using NVIDIA Nsight Systems and Nsight Compute
- Configure GPU hardware and software stacks to maximise runtime performance
- Evaluate and recommend appropriate GPU hardware for specific workloads
- Clearly communicate GPU-related opportunities and constraints to non-technical stakeholders
Required Skills, Knowledge & Experience
- Strong modern C++ development skills
- Proven experience with CUDA and CUDA libraries
- Solid understanding of software optimisation and performance tuning
- Experience developing and profiling GPU-accelerated applications
- Confidence working in performance-critical, real-time systems
Desirable (Not Essential)
- Knowledge of networking, streaming, or video compression
- Experience with real-time data pipelines or image processing systems
Working Environment
- Collaborative, cross-disciplinary engineering culture
- Close interaction with machine learning and research teams
- Informal and supportive workplace with an emphasis on sustainable workloads
Software Engineer C in Oxford employer: RECRUIT 12
Contact Detail:
RECRUIT 12 Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Software Engineer C in Oxford
✨Tip Number 1
Network like a pro! Reach out to folks in the industry on LinkedIn or at meetups. We all know that sometimes it’s not just what you know, but who you know that can land you that dream job.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your C++ projects, especially those involving GPU acceleration. We love seeing real-world applications of your expertise, so make sure to highlight any performance optimisations you've achieved.
✨Tip Number 3
Prepare for technical interviews by brushing up on CUDA and performance tuning. We recommend doing mock interviews with friends or using online platforms to get comfortable with the types of questions you might face.
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, we’re always looking for passionate individuals who fit our collaborative culture.
We think you need these skills to ace Software Engineer C in Oxford
Some tips for your application 🫡
Show Off Your C++ Skills: Make sure to highlight your modern C++ development skills in your application. We want to see how you've used C++ in real projects, especially if you've worked with GPU acceleration or CUDA. Don't hold back on showcasing your technical prowess!
Talk About Your GPU Experience: If you've got experience with GPU-accelerated applications, let us know! Share specific examples of how you've optimised performance or managed memory in your previous roles. This is your chance to shine and show us what you can bring to the table.
Keep It Clear and Concise: While we love detail, make sure your application is easy to read. Use clear language and structure your thoughts logically. Remember, we want to understand your experience without getting lost in jargon or overly complex explanations.
Apply Through Our Website: We encourage you to apply directly through our website. It’s the best way for us to receive your application and ensures you don’t miss out on any important updates. Plus, it shows us you're keen to join our team at StudySmarter!
How to prepare for a job interview at RECRUIT 12
✨Know Your C++ Inside Out
Make sure you brush up on your modern C++ skills before the interview. Be ready to discuss specific projects where you've implemented high-performance algorithms, especially using CUDA. They’ll want to see your depth of knowledge and how you can apply it in real-world scenarios.
✨Showcase Your GPU Expertise
Prepare to talk about your experience with GPU acceleration. Have examples ready that demonstrate your ability to manage host-device interactions and optimise data transfers. If you've worked with NVIDIA Nsight Systems or TensorRT, be sure to highlight those experiences!
✨Communicate Clearly
Since you'll be working closely with non-technical stakeholders, practice explaining complex GPU concepts in simple terms. This will show your ability to bridge the gap between technical and non-technical team members, which is crucial in a collaborative environment.
✨Emphasise Team Collaboration
This role values a collaborative culture, so be prepared to discuss how you've worked in teams before. Share examples of how you’ve contributed to group projects, especially in cross-disciplinary settings, and how you handle feedback and support from colleagues.
