Systems Research Engineer (AI Infrastructure & Distributed Systems

A pioneering research-focused engineering team is seeking Systems Research Engineers to help shape the next generation of AI-native 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.

Make your application after reading the following skill and qualification requirements for this position.

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-generation 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. xwzovoh
• Accelerate your career in one of the fastest-growing areas of technology.

If you’re a motivated and skilled professional ready for your next challenge, apply now or send your CV to