AI Research Engineer (Kernel & Inference Optimization)

Accountabilities • Design and deploy advanced model-serving architectures optimized for high throughput, low latency, and efficient memory utilization.

• Develop inference pipelines capable of operating effectively across diverse environments, including resource-constrained mobile devices and edge platforms.

• Establish clear performance targets covering response latency, token generation speed, throughput, memory footprint, and reliability.

• Build and execute controlled inference benchmarks in simulated and production environments, tracking latency, throughput, memory consumption, and error rates.

• Create and maintain representative datasets and simulation scenarios for evaluating model performance under real-world and resource-constrained conditions.

• Identify computational and memory bottlenecks across inference pipelines and implement solutions involving batching, networking, memory management, and other system-level optimizations.

• Develop custom GPU kernels and compute shaders for mobile hardware, including solutions written in Metal Shading Language (MSL).

• Apply advanced inference optimization techniques such as pruning, quantization, Flash Attention, KV caching, and speculative decoding.

• Design and optimize distributed inference systems using approaches such as tensor parallelism, pipeline parallelism, and expert parallelism for large-scale GPU workloads.

• Work with cross-functional engineering and research teams to integrate optimized inference frameworks into production and edge-device applications.

• Define evaluation methodologies, document experimental results, compare performance against established benchmarks, and continuously refine optimization strategies.

• Monitor production performance and use empirical research to identify opportunities for further improvements in scalability, efficiency, and reliability.

Requirements:

• Degree in Computer Science or a related technical field; a PhD in NLP, Machine Learning, or a related discipline is highly relevant, particularly with a strong AI research track record and publications at leading conferences.

• Proven expertise in Metal Shading Language (MSL), including the ability to write custom compute shaders from scratch.

• Demonstrated experience with low-level kernel optimization and inference optimization on mobile or other resource-constrained devices.

• Track record of delivering measurable improvements in inference latency, throughput, and memory footprint for domain-specific applications.

• Deep understanding of modern model-serving architectures, inference engines, and optimization techniques for high-performance AI deployment.

• Strong experience writing GPU kernels for mobile devices such as smartphones.

• Practical experience developing and deploying end-to-end inference pipelines, from model optimization through production integration on constrained hardware.

• Strong ability to apply empirical research and systematic experimentation to solve latency, computational, and memory challenges.

• Experience designing robust evaluation and benchmarking frameworks for inference systems.

• Knowledge of distributed inference techniques, including tensor parallelism, pipeline parallelism, and expert parallelism for large-scale GPU clusters.

• Deep understanding of the mathematical foundations and architecture of diffusion models and Vision Transformers.

• Familiarity with modern inference optimization techniques including pruning, quantization, Flash Attention, KV Cache optimization, and speculative decoding such as EAGLE.

• Strong analytical and problem-solving abilities, with an ability to investigate complex system bottlenecks and turn research findings into practical engineering solutions.

• Excellent English communication skills and the ability to collaborate effectively with distributed, cross-functional technical teams.

Benefits:

• Opportunity to work on advanced AI systems spanning model serving, inference optimization, mobile computing, edge deployment, and large-scale distributed inference.

• Remote-first working environment with an international team.

• Exposure to cutting-edge AI research and practical systems engineering challenges.

• Opportunity to contribute to performance-critical infrastructure where improvements can have a measurable impact on real-world AI applications.

• Collaborative environment combining research-driven experimentation with hands-on engineering.

• Opportunity to work with advanced model architectures including diffusion models, Vision Transformers, and multimodal systems.

• Access to challenging technical problems involving GPU kernels, inference engines, memory optimization, and distributed computing.

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