At a Glance
- Tasks: Optimise GPU performance for machine learning workloads using low-level C++ programming.
- Company: Disruptive tech firm leading in ML and GPU technology.
- Benefits: Health package, tech stipend, innovation days, and adventure days.
- Other info: Dynamic environment with opportunities for personal and professional growth.
- Why this job: Join a cutting-edge team shaping the future of AI and ML technologies.
- Qualifications: Proficient in C++, GPU architectures, and machine learning algorithms.
The predicted salary is between 60000 - 80000 £ per year.
We are partnering with an exciting, disruptive technology company at the forefront of machine learning (ML) and high-performance GPU computing. This innovative firm is leveraging cutting-edge GPU technology to optimize machine learning algorithms and computational models, powering the next wave of AI and data-driven applications. Their mission is to drive performance optimization in ML and AI workloads, transforming industries such as autonomous vehicles, healthcare, and immersive gaming experiences.
We are seeking a Low-Level C++ Engineer to join their team and work directly on optimizing GPU performance for machine learning (ML) workloads. As part of the ML optimization team, you will be responsible for developing and fine-tuning GPU-level solutions that accelerate machine learning training and inference. If you’re eager to work at the intersection of low-level GPU programming and machine learning, this is the role for you.
Key responsibilities include:
- Developing and optimizing low-level C++ code for GPU hardware to accelerate machine learning workloads
- Working closely with ML engineers to implement GPU-level optimizations for ML model training and inference
- Profiling and optimizing ML workloads running on GPUs, focusing on memory management, parallelization, and performance tuning
- Developing and optimizing custom GPU drivers and frameworks for ML-specific tasks
- Collaborating with data scientists and researchers to integrate new machine learning algorithms and enhance their GPU acceleration
- Staying up to date with the latest GPU architecture and machine learning advancements
Requirements:
- Proficiency in C++ with a strong focus on memory management, multi-threading, and low-level performance optimizations
- Experience with GPU architectures (e.g., NVIDIA, AMD) and programming frameworks like CUDA, OpenCL, and TensorFlow
- Understanding of machine learning algorithms, including model training and inference, and how to optimize these for GPU-based computation
- Strong knowledge of parallel computing, vectorization, and multi-core systems for high-performance computing (HPC)
- Experience with profiling tools (e.g., NVIDIA Nsight, gdb, perf) and performance tuning in a GPU environment
- Experience working with deep learning frameworks (e.g., TensorFlow, PyTorch) is a plus
- Strong problem-solving skills and a passion for AI and innovative technology
What’s on Offer:
- Comprehensive Health & Wellness Package including mental health support and fitness programs
- Tech Upgrade Stipend for personal setups (monitors, laptops, or VR headsets)
- Innovation Days to explore personal projects and experiment with new technologies
- Adventure Days: One paid day each quarter to engage in personal hobbies or exploration
- Market-leading learning and development with access to exclusive courses and conferences
- A unique opportunity to influence the future of AI and ML technologies at the cutting edge
If you are a Low-Level C++ Engineer looking to join one of the world’s most elite teams, please apply for more information.
GPU ML Optimization Engineer - Low-Level C++ employer: Hunter Bond
Join a pioneering technology firm in London that is redefining the landscape of machine learning and GPU computing. With a strong emphasis on employee well-being, the company offers a comprehensive health and wellness package, innovation days for personal projects, and adventure days to pursue your passions. This dynamic work culture fosters growth through market-leading learning opportunities, making it an exceptional place for those eager to make a meaningful impact in AI and ML technologies.
StudySmarter Expert Advice🤫
We think this is how you could land GPU ML Optimization Engineer - Low-Level C++
✨Tip Number 1
Network like a pro! Reach out to folks in the industry on LinkedIn or at tech meetups. You never know who might have the inside scoop on job openings or can put in a good word for you.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your projects, especially those related to GPU programming and machine learning. This will give potential employers a taste of what you can do.
✨Tip Number 3
Prepare for technical interviews by brushing up on your C++ knowledge and GPU optimization techniques. Practice coding challenges and be ready to discuss your past projects in detail.
✨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 love seeing candidates who are proactive about their job search.
We think you need these skills to ace GPU ML Optimization Engineer - Low-Level C++
Some tips for your application 🫡
Tailor Your CV:Make sure your CV highlights your experience with low-level C++ programming and GPU architectures. We want to see how your skills align with the role, so don’t be shy about showcasing relevant projects or achievements!
Craft a Compelling Cover Letter:Your cover letter is your chance to shine! Use it to explain why you’re passionate about GPU ML optimization and how your background makes you a perfect fit for our team. Keep it engaging and personal – we love to see your personality!
Showcase Your Problem-Solving Skills:In your application, highlight specific examples where you've tackled complex problems in GPU programming or machine learning. We’re looking for those who can think critically and innovate, so let us know how you’ve done this in the past!
Apply Through Our Website:We encourage you to apply directly through our website for the best chance of getting noticed. It’s super easy, and you’ll be able to keep track of your application status. Plus, we love seeing candidates who take that extra step!
How to prepare for a job interview at Hunter Bond
✨Know Your C++ Inside Out
Make sure you brush up on your C++ skills, especially around memory management and multi-threading. Be ready to discuss specific projects where you've optimised low-level code, as this will show your practical experience.
✨Familiarise Yourself with GPU Architectures
Since the role focuses on GPU optimisation, it’s crucial to understand different architectures like NVIDIA and AMD. Prepare to talk about how you've used CUDA or OpenCL in past projects, and be ready to answer technical questions related to these technologies.
✨Showcase Your Problem-Solving Skills
Be prepared to tackle some real-world problems during the interview. Think of examples where you've had to optimise ML workloads or troubleshoot performance issues. This will demonstrate your analytical thinking and ability to work under pressure.
✨Stay Updated on ML Trends
The tech world moves fast, especially in AI and ML. Make sure you’re aware of the latest advancements and trends in machine learning algorithms and GPU technology. This knowledge will not only impress your interviewers but also show your passion for the field.
