At a Glance
- Tasks: Design and optimise large-scale ML systems, collaborating with researchers on cutting-edge projects.
- Company: Join a leading quantitative research organisation at the forefront of AI technology.
- Benefits: Competitive salary, bonuses, and significant autonomy from day one.
- Other info: Work with top-tier researchers and enjoy deep investment in compute infrastructure.
- Why this job: Tackle unique technical challenges and make a real impact in the AI field.
- Qualifications: Strong experience in ML engineering, software skills in Python/C++, and knowledge of distributed systems.
The predicted salary is between 70000 - 90000 £ per year.
- We're partnering with a highly quantitative research organisation building some of the most advanced machine learning systems in industry.
- Engineers in this team operate at the intersection of machine learning, distributed systems, and high-performance computing, helping scale modern AI workloads across a large GPU estate.
The work spans distributed training, inference optimisation, compute infrastructure, systems design, and performance engineering.
- You'll work directly with researchers to take cutting-edge ML ideas from prototype to production, solving problems that span software, hardware, networking, compilers, and large-scale distributed systems.
- This is an opportunity to tackle technical challenges rarely seen outside leading AI labs and top-tier quantitative research firms.
Responsibilities
- Design and optimise large-scale training and inference systems for modern ML workloads.
- Improve throughput, latency, GPU utilisation and training efficiency across distributed environments.
- Build infrastructure and tooling that accelerates experimentation and model development.
- Partner with researchers to productionise novel ML approaches.
- Drive performance improvements across software, hardware and networking layers.
- Influence the technical direction of critical ML infrastructure used across the organisation.
What We're Looking For
- Strong experience in Machine Learning Engineering, Research Engineering, ML Infrastructure, Distributed Systems or Performance Engineering.
- Excellent software engineering skills in Python and/or C++.
- Experience working with modern ML frameworks such as Py Torch, JAX or Tensor Flow.
- Experience training, deploying or optimising large-scale machine learning models.
- Strong understanding of distributed systems, parallel computing and performance optimisation.
- Degree in Computer Science, Mathematics, Physics, Engineering or a related quantitative discipline, or equivalent industry experience.
- Particularly Relevant Experience
- Large-scale distributed training (Deep Speed, FSDP, Megatron, Ray, DDP or similar).
- GPU programming and optimisation (CUDA, Triton, NCCL, XLA, PTX).
- Multi-GPU or multi-node training environments.
- HPC, Kubernetes, Slurm or large-scale compute infrastructure.
- Foundation models, LLMs, recommendation systems or large-scale deep learning.
- Compiler technologies, kernel optimisation, inference optimisation or systems-level ML performance work.
Why Join?
- Work on some of the largest and most computationally intensive ML workloads in industry.
- Solve challenging problems across distributed systems, GPU computing, machine learning infrastructure and performance optimisation.
- Collaborate closely with exceptional researchers, engineers and quantitative scientists.
- Significant autonomy and ownership from day one.
- Deep investment in compute infrastructure and engineering excellence.
- Competitive compensation and bonus structure.
- #J-18808-Ljbffr
Machine Learning Specialist employer: Stanford Black Limited
Join a leading systematic trading firm in London, where you'll thrive in a technology-led culture that fosters innovation and collaboration. With a flat organisational structure and direct access to senior leadership, you'll have the opportunity to make a meaningful impact while working on challenging infrastructure projects. Enjoy exceptional benefits including free meals, a generous pension scheme, and a strong focus on work-life balance, all within a vibrant new HQ equipped with modern amenities.
StudySmarter Expert Advice🤫
We think this is how you could land Machine Learning Specialist
✨Get Involved in Data Science Meetups
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Start building a public portfolio showcasing your data science projects on platforms like GitHub or personal websites. Highlight unique analyses or models you've developed. This not only demonstrates your skills but also gets your name out there for roles like Machine Learning Specialist at Stanford Black Limited.
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Join professional bodies related to data science, like the Data Science Society or similar organisations. Getting involved can lead to mentorship opportunities and insider knowledge about full-time positions at companies like Stanford Black Limited.
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We think you need these skills to ace Machine Learning Specialist
Some tips for your application 🫡
Show Off Your Projects:In the world of data science, your projects can speak volumes about your skills. Make sure to showcase a few key projects in your CV or portfolio, especially those that highlight your ability to work with data sets, build models, or use relevant tools like Python, R, or SQL. Don’t forget to include links to any GitHub repositories if applicable!
Quantify Your Achievements:Employers love numbers! When drafting your CV, highlight your achievements with quantifiable results. For instance, mention how your data analysis led to a certain percentage increase in efficiency or revenue at a previous job or project. These details can really make your application pop!
Craft a Tailored Cover Letter:For a full-time role at Stanford Black Limited, your cover letter should reflect your passion for data science and your excitement about the specific projects or values of the company. Dive into why you’re a good fit, how your skills align with their needs, and any unique perspectives you can bring to the team.
Stand Out with Relevant Courses and Certifications:Although experience talks, relevant courses or certifications can be your ticket to impressing hiring managers at Stanford Black Limited. Mention any standout courses you've completed that equipped you with essential skills, such as machine learning certifications or data visualisation courses. This shows your commitment to continuously developing your skills in the field!
How to prepare for a job interview at Stanford Black Limited
✨Brush Up on Your Statistics
For a data science role, we need to seriously sharpen our statistics skills. Get ready to tackle technical questions on probability distributions, hypothesis testing, and regression analysis. These are often the bread and butter of data science interviews, so don't just skim over them!
✨Showcase Your Projects
Prepare a killer portfolio showcasing your data science projects. We should include details about the datasets used, the tools and techniques applied, and the impact of your findings. If we can walk them through a particularly challenging project or a cool visualisation that had real-world implications, it’ll really make us stand out!
✨Get Comfortable with Python and R
Most data science positions require us to be proficient in programming languages like Python and R. We should practice common libraries like pandas, NumPy, and scikit-learn, and be ready for live coding exercises or algorithm questions. Showing off our coding chops can really impress the interviewers at Stanford Black Limited!
✨Prepare for Case Studies
Expect to encounter real-world case studies during the interview. We might be asked how we’d approach a data problem or analyse a dataset to extract insights. It's essential to think out loud and demonstrate our problem-solving process so that the interviewer can see our logical thinking in action.
