AI Research Engineer – Kernel, Inference Optimization

• Drive innovation in model serving and inference architectures for advanced AI systems.
• Focus on optimizing model deployment and inference strategies to deliver highly responsive, efficient, and scalable performance across real-world applications.
• Work on a wide spectrum of systems, ranging from resource‑efficient models designed for limited hardware environments to complex, multi‑modal architectures that integrate data such as text, images, and audio.
• Develop, test, and implement novel serving strategies and inference algorithms.
• Engineer robust inference pipelines, establish comprehensive performance metrics, and identify and resolve bottlenecks in production environments.
• Enable high‑throughput, low‑latency, low‑memory footprint, and scalable AI performance that delivers tangible value in dynamic, real‑world scenarios.

Requirements

• A degree in Computer Science or related field.
• Ideally Ph D in NLP, Machine Learning, or a related field, complemented by a solid track record in AI R&D (with good publications in A* conferences).
• Must have knowledge of Metal Shading Language (MSL).
• Proven experience in low‑level kernel optimizations and inference optimization on mobile devices is essential.
• Your contributions should have led to measurable improvements in inference latency, throughput, and memory footprint for domain‑specific applications, particularly on resource‑constrained devices and edge platforms.
• A deep understanding of modern model serving architectures and inference optimization techniques is required.
• Must have strong expertise in writing GPU kernels for mobile devices (i. e., smartphones) as well as a deep understanding of model serving frameworks and engines.
• Practical experience in developing and deploying end‑to‑end inference pipelines, from optimizing models for efficient serving to integrating these solutions on resource‑constrained devices is required.
• Demonstrated ability to apply empirical research to overcome challenges in model serving, such as latency optimization, computational bottlenecks, and memory constraints.
• You should be proficient in designing robust evaluation frameworks and iterating on optimization strategies to continuously push the boundaries of inference performance and system efficiency.
• Distributed
• Inference

Systems: Designing and optimizing high‑performance inference engines using techniques like Tensor Parallelism, Pipeline Parallelism, and Expert Parallelism to handle massive models on GPU clusters.

• Deep understanding of the math and structure behind Diffusion Models and Vision Transformers
• Understanding on Pruning, Quantization, Flash attention, KV Cache, Speculative Decoding (Eagle) etc.
• ATS Optimization Keywords
• Hard Skills
• Metal Shading Language
• GPU kernels
• inference optimization
• model serving architectures
• end-to-end inference pipelines
• latency optimization
• Tensor Parallelism
• Pipeline Parallelism
• Expert Parallelism
• Diffusion Models
• Soft Skills
• innovation
• problem-solving
• empirical research application
• evaluation framework design
• optimization strategy iteration

Certifications & Qualifications

• Ph D in NLP
• Ph D in Machine Learning
• Industry Keywords
• AI systems
• resource-constrained devices
• edge platforms
• performance metrics
• computational bottlenecks
• memory constraints
• Pruning
• Quantization
• Flash attention
• KV Cache
• #J-18808-Ljbffr