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- Tasks: Build and experiment with cutting-edge distributed systems for AI workloads.
- Company: Pioneering research-focused engineering team in Edinburgh.
- Benefits: Competitive salary, career growth, and collaboration with global experts.
- Other info: Opportunity to publish and influence future technology directions.
- Why this job: Shape the future of AI infrastructure and tackle real-world challenges.
- Qualifications: PhD in relevant field and strong foundation in distributed systems.
The predicted salary is between 60000 - 80000 £ per year.
A pioneering research-focused engineering team is seeking Systems Research Engineers to help shape the next of AI infrastructure. This role sits at the intersection of systems research and large-scale engineering, focusing on distributed architectures that support the training, serving, and deployment of advanced AI models.
The rapid evolution of large-scale AI models is transforming how modern computing systems are designed and deployed. A highly advanced research-driven engineering group is building the next wave of infrastructure that powers intelligent systems at scale—redefining how models are trained, served, and optimised across distributed environments.
This role offers a unique blend of hands-on engineering and forward-looking research, ideal for engineers who want to push the boundaries of distributed systems and AI infrastructure while working on real-world, high-impact platforms.
What You’ll Work On
- Build and experiment with distributed system components tailored for data-intensive and AI-driven workloads.
- Design scalable infrastructure capable of operating across diverse hardware environments including CPUs, GPUs, and accelerators.
- Develop high-performance model serving systems with a focus on efficiency, scalability, and resilience.
- Analyse system behaviour using profiling tools to uncover performance bottlenecks and optimisation opportunities.
- Improve memory usage, caching strategies, and scheduling efficiency in large-scale inference systems.
- Create solutions that enable low-latency, multi-tenant AI services in distributed environments.
- Explore and prototype new approaches to inference architecture and cluster-level orchestration.
- Translate technical innovations into tangible outcomes, including internal adoption and external publications.
- Work closely with global teams to shape long-term infrastructure direction and strategy.
What You’ll Bring
- PhD in Computer Science, Electrical Engineering, or a related discipline.
- Strong foundation in distributed systems and operating systems principles.
- Understanding of machine learning infrastructure and large-scale model serving.
- Experience with systems-level programming in C/C++.
- Proficiency in Python for experimentation and rapid prototyping.
- Familiarity with distributed algorithms and system design trade-offs.
- Experience using performance analysis and profiling tools.
- Ability to communicate complex ideas clearly and work effectively in collaborative environments.
- Doctoral research in distributed systems, large-scale infrastructure, or AI platforms.
- Contributions to recognised systems or machine learning conferences.
- Hands-on experience with load balancing, fault tolerance, or cluster scheduling.
- Exposure to distributed caching, state management, or high-performance cloud systems.
- Experience building or optimising large-scale AI or cloud infrastructure.
Why This Role Stands Out
- Be part of a team shaping the infrastructure behind next-AI systems.
- Work on problems that combine deep technical research with real-world deployment.
- Gain exposure to cutting-edge architectures in distributed computing and AI.
- Collaborate with globally recognized experts in systems and machine learning.
- Opportunity to publish, innovate, and influence future technology directions.
- Accelerate your career in one of the fastest-growing areas of technology.
Systems Research Engineer (AI Infrastructure & Distributed Systems in Edinburgh employer: Energy Jobline ZR
Contact Detail:
Energy Jobline ZR Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Systems Research Engineer (AI Infrastructure & Distributed Systems in Edinburgh
✨Network Like a Pro
Get out there and connect with folks in the industry! Attend meetups, conferences, or even online webinars related to AI and distributed systems. You never know who might have a lead on your dream job or can offer valuable insights.
✨Show Off Your Skills
Don’t just talk about your experience—show it! Create a portfolio showcasing your projects, especially those related to distributed systems or AI infrastructure. This will give potential employers a taste of what you can bring to the table.
✨Ace the Interview
Prepare for technical interviews by brushing up on your knowledge of distributed systems and AI concepts. Practice coding challenges and be ready to discuss your past projects in detail. Confidence is key, so believe in your skills!
✨Apply Through Our Website
Make sure to apply directly through our website for the best chance at landing that Systems Research Engineer role. We love seeing candidates who are genuinely interested in joining our pioneering team!
We think you need these skills to ace Systems Research Engineer (AI Infrastructure & Distributed Systems in Edinburgh
Some tips for your application 🫡
Tailor Your CV: Make sure your CV reflects the skills and experiences that align with the Systems Research Engineer role. Highlight your expertise in distributed systems, AI infrastructure, and any relevant projects you've worked on. We want to see how you can contribute to our pioneering team!
Craft a Compelling Cover Letter: Your cover letter is your chance to shine! Use it to explain why you're passionate about AI infrastructure and how your background makes you a perfect fit for us. Be sure to mention any hands-on experience or research that relates to the job description.
Showcase Your Projects: If you've worked on any relevant projects, whether academic or personal, make sure to include them in your application. We love seeing practical examples of your work, especially those that demonstrate your understanding of distributed systems and AI models.
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 submit all your materials in one go. Plus, it shows us you're serious about joining our innovative team!
How to prepare for a job interview at Energy Jobline ZR
✨Know Your Stuff
Make sure you brush up on your knowledge of distributed systems and AI infrastructure. Be ready to discuss your PhD research and how it relates to the role. Familiarity with performance analysis tools and system design trade-offs will definitely give you an edge.
✨Showcase Your Hands-On Experience
Prepare to talk about any practical projects you've worked on, especially those involving large-scale AI or cloud infrastructure. Highlight your experience with load balancing, fault tolerance, and cluster scheduling. Real-world examples will help demonstrate your capabilities.
✨Communicate Clearly
Since this role involves collaboration with global teams, practice explaining complex ideas in a simple way. You might be asked to present your thoughts on a technical problem, so clarity and confidence are key. Think about how you can convey your insights effectively.
✨Be Ready to Innovate
This position is all about pushing boundaries, so come prepared with ideas on new approaches to inference architecture or cluster-level orchestration. Showing that you can think outside the box and contribute to future technology directions will impress the interviewers.