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- Tasks: Design high-performance algorithms and optimise GPU workloads for cutting-edge applications.
- Company: Join a collaborative tech company focused on innovation and work-life balance.
- Benefits: Enjoy a supportive workplace with opportunities for professional growth and development.
- Why this job: Make an impact in entertainment, engineering, and science with your coding skills.
- Qualifications: Strong C++ skills and experience with CUDA required; passion for performance optimisation is a plus.
- Other info: Work closely with machine learning experts in a dynamic, cross-disciplinary environment.
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
- 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
- Knowledge of networking, streaming, or video compression
- Experience with real-time data pipelines or image processing systems
- 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 employer: RECRUIT 12
Contact Detail:
RECRUIT 12 Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Software Engineer C
✨Tip Number 1
Network like a pro! Reach out to folks in the industry, attend meetups, and connect with potential colleagues on LinkedIn. You never know who might have the inside scoop on job openings or can refer you directly.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your projects, especially those involving C++ and GPU acceleration. This will give you an edge and demonstrate your hands-on experience to potential employers.
✨Tip Number 3
Prepare for technical interviews by brushing up on your algorithms and data structures. Practice coding challenges that focus on performance optimisation and GPU-related tasks to impress your interviewers.
✨Tip Number 4
Don’t forget to apply through our website! We love seeing applications from passionate candidates who are eager to join our collaborative team. Plus, it’s a great way to ensure your application gets the attention it deserves.
We think you need these skills to ace Software Engineer C
Some tips for your application 🫡
Show Off Your Skills: Make sure to highlight your C++ and GPU acceleration expertise in your application. We want to see how your experience aligns with the role, so don’t hold back on showcasing your projects or achievements!
Tailor Your Application: Take a moment to customise your CV and cover letter for this specific role. Mention relevant experiences that relate to CUDA, machine learning, and performance optimisation. It helps us see why you’re the perfect fit!
Be Clear and Concise: When writing your application, keep it straightforward and to the point. We appreciate clarity, so avoid jargon unless it’s necessary. Remember, we want to understand your skills without getting lost in technical details.
Apply Through Our Website: We encourage you to submit your application through our website. It’s the best way for us to receive your details and ensures you’re considered for the role. Plus, it’s super easy to do!
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 memory management. If you’ve worked with NVIDIA Nsight Systems or TensorRT, be sure to highlight those experiences!
✨Communicate Clearly
Since you'll need to explain complex GPU-related concepts to non-technical stakeholders, practice simplifying your explanations. Think of ways to convey technical details in a way that’s easy to understand. This will show your ability to bridge the gap between tech and business.
✨Emphasise Collaboration
This role is all about teamwork, so be prepared to discuss how you’ve successfully collaborated with cross-disciplinary teams in the past. Share examples of how you’ve worked closely with machine learning or research specialists to achieve common goals.
