About the role
We are seeking Systems Research Engineers with a strong interest in computer systems, distributed AI infrastructure, and performance optimization. These roles are ideal for recent PhD graduates or exceptional BSc/MSc engineers looking to build research-driven engineering experience in areas such as operating systems, distributed systems, AI model serving, and machine learning infrastructure. You will work closely with senior architects on real-world projects, helping to prototype and optimize next-generation AI infrastructure.
Check you match the skill requirements for this role, as well as associated experience, then apply with your CV below.
Required Qualifications and Skills:
Bachelors or Masters degree in Computer Science, Electrical Engineering, or related field.
Strong knowledge of distributed systems, operating systems, machine learning systems architecture, Inference serving, and AI Infrastructure.
Hands-on experience with LLM serving frameworks (e.g., vLLM, Ray Serve, TensorRT-LLM, TGI) and distributed KV cache optimization.
Proficiency in C/C++, with additional experience in Python for research prototyping.
Solid grounding in systems research methodology, distributed algorithms, and profiling tools.
Team-oriented mindset with effective technical communication skills.
Desired Qualifications and Experience:
PhD in systems, distributed computing, or large-scale AI infrastructure.
Publications in top-tier systems or ML conferences (NSDI, OSDI, EuroSys, SoCC, MLSys, NeurIPS, ICML, ICLR).
Understanding of load balancing, state management, fault tolerance, and resource scheduling in large-scale AI inference clusters. xwzovoh
Prior experience designing, deploying, and profiling high-performance cloud or AI infrastructure systems.
Check you match the skill requirements for this role, as well as associated experience, then apply with your CV below.
Required Qualifications and Skills:
Bachelors or Masters degree in Computer Science, Electrical Engineering, or related field.
Strong knowledge of distributed systems, operating systems, machine learning systems architecture, Inference serving, and AI Infrastructure.
Hands-on experience with LLM serving frameworks (e.g., vLLM, Ray Serve, TensorRT-LLM, TGI) and distributed KV cache optimization.
Proficiency in C/C++, with additional experience in Python for research prototyping.
Solid grounding in systems research methodology, distributed algorithms, and profiling tools.
Team-oriented mindset with effective technical communication skills.
Desired Qualifications and Experience:
PhD in systems, distributed computing, or large-scale AI infrastructure.
Publications in top-tier systems or ML conferences (NSDI, OSDI, EuroSys, SoCC, MLSys, NeurIPS, ICML, ICLR).
Understanding of load balancing, state management, fault tolerance, and resource scheduling in large-scale AI inference clusters. xwzovoh
Prior experience designing, deploying, and profiling high-performance cloud or AI infrastructure systems.
About this listing
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