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.