At a Glance
- Tasks: Develop and optimise cutting-edge AI workloads on next-gen accelerator hardware.
- Company: Join KRAI, a leader in AI infrastructure optimisation with a collaborative culture.
- Benefits: Work with top tech companies and contribute to impactful open-source projects.
- Other info: Join a small, friendly team with deep technical expertise and great career growth.
- Why this job: Be at the forefront of AI technology and make a real-world impact.
- Qualifications: Advanced degree in Computer Engineering or related field with 3+ years experience.
The predicted salary is between 70000 - 90000 £ per year.
About Us
KRAI is a cutting-edge AI infrastructure optimization company, a proven and valuable strategic partner for top accelerator designers, server manufacturers, and cloud providers.
We are a Founding Member of the non-profit MLCommons consortium, actively contributing to community research and open-source efforts for AI Systems.
We are looking for exceptional R&D engineers to advance the state-of-the-art in AI accelerator programming (accelerating acceleration).
The core challenge?
Mapping rapidly evolving AI workloads onto rapidly evolving AI accelerator hardware (next generation accelerators, as well as traditional GPUs), while navigating an infinite space of performance, quality, and cost trade-offs.
Our approach combines rigorous performance engineering with systematic agentic techniques. We aim for results that genuinely surprise even seasoned professionals!
What You'll Do
- Developing and optimizing low-level compute kernels for the latest AI workloads.
- Working across a range of accelerator architectures, including hardware that is years from public release.
- Exploring performance, efficiency, and quality trade-offs.
- Driving full-stack inference optimization: from AI models all the way down to hardware.
- Applying both traditional performance engineering tools and frontier AI techniques to solve complex optimization problems.
- Collaborating with top accelerator designers, server manufacturers and cloud providers to deliver best-in-class performance results.
What We're Looking For
- Advanced degree (MSc or Ph D) in Computer Engineering, Computer Science, or Natural Sciences.
- 3+ years of hands-on experience optimizing compute-intensive workloads on accelerator hardware (GPUs, TPUs, NPUs, etc).
- Experience with full-stack AI inference optimization: from models to runtimes to kernels.
- Strong command of performance engineering tools: compilers, debuggers, profilers, simulators, and roofline analysis.
- Workflow automation and reproducibility as first-class concerns.
- Strong communication and collaboration skills.
- What We're NOT Looking For
- We do NOT design AI hardware: we optimize software that runs on our customers' hardware.
- We do NOT design AI pipelines: we get down to the nitty-gritty of AI inference.
- Why KRAI
- Always at the bleeding edge: working with the SOTA AI models and pre-release accelerator hardware.
- Real-world impact: directly influencing hardware roadmaps and procurement decisions at major technology companies.
- Active contributions to open-source and research: getting high visibility and recognition in the AI Systems community.
- Small well-knit team with deep technical expertise and friendly culture.
R&D in AI Accelerator Optimization in Cambridge employer: KRAI
At KRAI, we pride ourselves on being at the forefront of AI infrastructure innovation, offering our R&D engineers a dynamic work environment that fosters creativity and collaboration. Our commitment to employee growth is evident through continuous learning opportunities and partnerships with leading hardware designers, ensuring that you are always working with the latest technologies in a supportive culture that values your contributions. Located in a vibrant tech hub, KRAI provides unique advantages such as access to industry events and networking opportunities, making it an excellent place for those seeking meaningful and rewarding employment.
StudySmarter Expert Advice🤫
We think this is how you could land R&D in AI Accelerator Optimization in Cambridge
✨Get Involved in Data Science Meetups
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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 KRAI.
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We think you need these skills to ace R&D in AI Accelerator Optimization in Cambridge
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 KRAI, 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 KRAI. 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 KRAI
✨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 KRAI!
✨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.
