AI Research Engineer (Kernel & Inference Optimization) in UK at Jobgether

This position is posted by Jobgether on behalf of a partner company. We are currently looking for a AI Research Engineer (Kernel & Inference Optimization) in United Kingdom.

This is an exciting opportunity for a highly technical AI engineer to contribute to the next generation of scalable and high-performance inference systems powering real-world AI applications. In this role, you will work on optimizing model serving architectures, improving latency and throughput, and enhancing deployment efficiency across cloud, edge, and resource-constrained environments. You will collaborate with globally distributed engineering and research teams focused on advanced AI systems, multi-modal architectures, and infrastructure innovation. The position offers a research-driven environment where experimentation, benchmarking, and performance optimization are central to daily work. Ideal candidates are passionate about low-level optimization, inference scalability, and building robust AI systems that deliver measurable production impact at scale.

• Design, develop, and optimize advanced model serving architectures focused on high throughput, low latency, and efficient memory utilization.
• Build scalable inference pipelines capable of running across cloud, edge, and resource-constrained environments.
• Conduct controlled inference experiments in simulated and production environments to evaluate system performance and reliability.
• Monitor and analyze key performance metrics such as latency, throughput, memory consumption, token response time, and error rates.
• Develop and maintain benchmarking methodologies and performance validation frameworks for AI inference systems.
• Identify bottlenecks in serving pipelines, including batch processing inefficiencies, network overhead, and excessive memory usage.
• Optimize inference frameworks and deployment strategies for scalability, resilience, and operational efficiency.
• Collaborate with cross-functional engineering and research teams to integrate optimized inference solutions into production environments.
• Create high-quality testing datasets and deployment scenarios that reflect real-world operational challenges.
• Continuously improve inference infrastructure through experimentation, iteration, and adoption of cutting-edge AI serving techniques.

Requirements:

• Strong experience in AI/ML engineering with a focus on inference optimization, model serving, or AI systems performance.
• Deep understanding of model deployment architectures and inference frameworks for large-scale AI applications.
• Expertise in optimizing latency, throughput, scalability, and memory footprint in production AI systems.
• Hands-on experience with performance monitoring, benchmarking, profiling, and bottleneck analysis.
• Strong knowledge of advanced AI model architectures, including multi-modal systems and resource-efficient models.
• Experience building and deploying AI systems across cloud, edge, or low-resource hardware environments.
• Proficiency in programming languages commonly used in AI infrastructure and optimization workflows.
• Strong analytical and problem-solving abilities with a research-oriented mindset.
• Ability to work independently in a highly distributed and fast-moving global environment.
• Excellent English communication skills and ability to collaborate across technical and non-technical teams.
• Passion for innovation, experimentation, and scalable AI infrastructure development.

Benefits:

• Fully remote global work environment with flexible location options.
• Opportunity to work on cutting-edge AI, blockchain, and fintech technologies.
• Collaborative international team of highly skilled engineers and researchers.
• Exposure to innovative projects involving AI infrastructure, digital finance, and decentralized technologies.
• High-impact role with significant technical ownership and influence on product direction.
• Fast-paced and innovation-driven culture focused on experimentation and growth.
• Opportunities for continuous learning and professional development.
• Work environment that values autonomy, creativity, and technical excellence.
• Participation in projects with global reach and real-world scalability challenges.