AI Research Engineer – Kernel, Inference Optimization
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Job Description
AI Research Engineer at Tether focusing on optimizing model serving and inference architectures for advanced AI systems. Designing scalable performance solutions affecting real-world applications.
Responsibilities:
- 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 PhD 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 of Pruning, Quantization, Flash attention, KV Cache, Speculative Decoding (Eagle) etc.
Benefits:
- Flexible working hours
- Professional development opportunities
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