At a Glance
- Tasks: Optimise machine learning models for performance in a fast-paced trading environment.
- Company: Join Jane Street, a leader in finance and technology innovation.
- Benefits: Competitive salary, flexible work options, and opportunities for professional growth.
- Other info: Dynamic team culture where curiosity and innovation are valued.
- Why this job: Make a real impact by solving complex problems with cutting-edge ML techniques.
- Qualifications: Experience in low-level systems programming and modern ML tools.
The predicted salary is between 60000 - 80000 £ per year.
We are looking for an engineer with experience in low-level systems programming and optimisation to join our growing ML team. Machine learning is a critical pillar of Jane Street's global business. Our ever-evolving trading environment serves as a unique, rapid-feedback platform for ML experimentation, allowing us to incorporate new ideas with relatively little friction.
Your part here is optimising the performance of our models – both training and inference. We care about efficient large-scale training, low-latency inference in real-time systems and high-throughput inference in research. Part of this is improving straightforward CUDA, but the interesting part needs a whole-systems approach, including storage systems, networking and host- and GPU-level considerations. Zooming in, we also want to ensure our platform makes sense even at the lowest level – is all that throughput actually goodput? Does loading that vector from the L2 cache really take that long?
If you've never thought about a career in finance, you're in good company. Many of us were in the same position before working here. If you have a curious mind and a passion for solving interesting problems, we have a feeling you'll fit right in.
What we’re looking for:
- An understanding of modern ML techniques and toolsets.
- The experience and systems knowledge required to debug a training run's performance end to end.
- Low-level GPU knowledge of PTX, SASS, warps, cooperative groups, Tensor Cores and the memory hierarchy.
- Debugging and optimisation experience using tools like CUDA GDB, NSight Systems, NSight Compute and nsight-compute.
- Library knowledge of Triton, CUTLASS, CUB, Thrust, cuDNN and cuBLAS.
- Intuition about the latency and throughput characteristics of CUDA graph launch, tensor core arithmetic, warp-level synchronization and asynchronous memory loads.
- Background in Infiniband, RoCE, GPUDirect, PXN, rail optimisation and NVLink, and how to use these networking technologies to link up GPU clusters.
- An understanding of the collective algorithms supporting distributed GPU training in NCCL or MPI.
- An inventive approach and the willingness to ask hard questions about whether we're taking the right approaches and using the right tools.
- Fluency in English.
Machine Learning Performance Engineer in London employer: Quant Blueprint LLC
At Jane Street, we pride ourselves on being an exceptional employer, offering a dynamic work environment that fosters innovation and collaboration. Our culture encourages curiosity and problem-solving, providing ample opportunities for professional growth within the fast-paced world of finance. Located in a vibrant city, our team enjoys a unique blend of cutting-edge technology and a supportive atmosphere, making it an ideal place for those passionate about machine learning and optimisation.
StudySmarter Expert Advice🤫
We think this is how you could land Machine Learning Performance Engineer in London
✨Tip Number 1
Network like a pro! Attend industry meetups, conferences, or online webinars related to machine learning and systems programming. Engaging with professionals in the field can open doors and give you insights that might just land you that dream job.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your projects, especially those involving CUDA and ML optimisation. Share it on platforms like GitHub and make sure to highlight any innovative solutions you've come up with – this will catch the eye of recruiters.
✨Tip Number 3
Prepare for technical interviews by brushing up on your low-level systems knowledge. Practice explaining complex concepts clearly and concisely, as you'll likely need to demonstrate your understanding of GPU architectures and performance optimisation techniques.
✨Tip Number 4
Don’t forget to apply through our website! We’re always on the lookout for curious minds who are passionate about solving problems. Your next opportunity could be just a click away, so get your application in and let’s see what you’ve got!
We think you need these skills to ace Machine Learning Performance Engineer in London
Some tips for your application 🫡
Tailor Your CV:Make sure your CV highlights your experience with low-level systems programming and optimisation. 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 machine learning and how your background makes you a great fit for our team. Keep it engaging and personal – we love to see your personality come through.
Showcase Your Technical Skills:When detailing your technical skills, be specific! Mention your experience with CUDA, debugging tools, and any relevant libraries. We’re keen to know how you’ve tackled performance issues in the past, so share those stories!
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’re considered for the role. Plus, it’s super easy – just follow the prompts and submit your materials!
How to prepare for a job interview at Quant Blueprint LLC
✨Know Your Tech Inside Out
Make sure you brush up on your knowledge of modern ML techniques and tools. Be ready to discuss your experience with CUDA, PTX, and SASS, as well as any debugging tools like CUDA GDB or NSight. The more familiar you are with these technologies, the better you'll be able to showcase your skills.
✨Showcase Your Problem-Solving Skills
Prepare to talk about specific challenges you've faced in optimising machine learning models. Think of examples where you improved performance or solved a tricky issue. This will demonstrate your inventive approach and ability to tackle complex problems, which is exactly what they’re looking for.
✨Understand the Bigger Picture
Don’t just focus on low-level programming; show that you understand how everything fits together. Be ready to discuss how storage systems, networking, and GPU-level considerations impact model performance. This holistic view will impress interviewers and show that you can think critically about system optimisation.
✨Ask Thoughtful Questions
Prepare some insightful questions about their current ML projects or the technologies they use. This not only shows your curiosity but also your willingness to engage in meaningful discussions. Asking about their approaches to latency and throughput can spark a great conversation and highlight your interest in the role.
