At a Glance
- Tasks: Optimise machine learning models for performance in a fast-paced trading environment.
- Company: Join Jane Street, a leading firm at the intersection of finance and technology.
- Benefits: Competitive salary, flexible work options, and opportunities for professional growth.
- Other info: Dynamic team culture that values curiosity and innovative thinking.
- 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 employer: Quant Blueprint LLC
Join our dynamic team as a Senior Lead Software Engineer in the heart of the financial district, where innovation meets expertise. We pride ourselves on fostering a collaborative work culture that encourages professional growth and mentorship, ensuring you have the resources to excel in your role while making impactful contributions to model risk management. With competitive benefits and a commitment to employee development, this is an exceptional opportunity for those seeking a meaningful career in a leading financial institution.
StudySmarter Expert Advice🤫
We think this is how you could land Machine Learning Performance Engineer
✨Join Local Tech Meetups
Get out there and mingle with fellow developers by joining local tech meetups. It’s a fantastic way to meet people who might be working at Quant Blueprint LLC or know someone who does. Plus, you can pick up some trendy tech skills and trends while you're at it!
✨Contribute to Open Source Projects
Show off your coding chops by jumping into open-source projects. Not only does this give you practical experience, but it also gets you noticed in the dev community. You'll create a killer portfolio that speaks volumes about your skills to Quant Blueprint LLC.
✨Tap into Online Developer Communities
Don’t underestimate the power of online developer communities like GitHub, Stack Overflow, and even Reddit. Participate in discussions, share your projects, and build your visibility. We can often find opportunities through these channels that can lead to a full-time gig at companies like Quant Blueprint LLC.
✨Explore Job Boards Specifically for Tech Roles
Keep your eyes peeled on job boards that focus on tech roles. Sites like TechCareers or Stack Overflow Jobs can often have listings for companies like Quant Blueprint LLC that might not show up on broader job sites. Make it a habit to check these regularly, and don’t hesitate to apply directly through our website!
We think you need these skills to ace Machine Learning Performance Engineer
Some tips for your application 🫡
Show off your coding skills:When applying for a software engineering role, it's super important to showcase your coding skills. Make sure your CV includes your tech stack, any relevant programming languages you’re comfortable with, and examples of projects you've worked on. If you have a GitHub profile, link it up! We love to see code in action.
Tailor your portfolio:For a full-time role, we’d expect to see some solid examples of your work in your portfolio. Make sure to include at least two or three projects that highlight your problem-solving skills and your ability to work with different technologies. Focus on the projects that are most relevant to the position at Quant Blueprint LLC.
Craft a killer cover letter:Your cover letter is your chance to stand out—make it personal! Explain why you want to work at Quant Blueprint LLC and how your skills align with the role. Show us your passion for software development. We dig enthusiastic candidates who understand the value of collaboration and continuous learning!
Be clear and concise:When it comes to writing your CV and cover letter, clarity is key. Avoid jargon that could confuse us and stick to simple, direct language. Highlight your achievements with quantifiable results where possible, and keep everything easy to read. A well-organised application goes a long way!
How to prepare for a job interview at Quant Blueprint LLC
✨Brush Up on Your Coding Skills
For a full-time software engineering role, it's crucial that we stay sharp with our coding abilities. Expect technical questions that might involve solving problems on the spot or discussing algorithms. Practise on platforms like LeetCode or HackerRank to get comfortable with the types of questions that often come up.
✨Know Your Tools and Frameworks
Make sure we’re well-acquainted with the tools and technologies listed in the job description. Familiarise ourselves with any specific frameworks or programming languages mentioned. If Quant Blueprint LLC uses React or Node.js, for instance, be ready to discuss how we’ve used them in previous projects or coursework.
✨Showcase Your Projects
Bring along a portfolio that highlights our best work. This could be code samples, GitHub repositories, or any side projects we’ve built. Make sure we can talk through our thought process for each project, especially the challenges we faced and how we solved them—this shows our problem-solving skills in action.
✨Prepare for Behavioural Questions
While technical skills are key, full-time positions also require cultural fit. Be ready to discuss our previous experiences and how we handle teamwork, conflict, and deadlines. Brush up on the STAR method—Situation, Task, Action, Result—to clearly articulate our past experiences when discussing how we've contributed to a team.
