AI Research Engineer, Kernel & Inference Optimization
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Job Description
AI Research Engineer driving innovation in model serving and inference architectures for advanced AI systems at Tether. Focusing on optimizing models for responsive, efficient, and scalable AI performance.
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
- Work on a wide spectrum of systems, ranging from resource-efficient models designed for limited hardware environments to complex, multi-modal architectures
- Engineering robust inference pipelines, establishing comprehensive performance metrics, and identifying and resolving bottlenecks
- Enable high-throughput, low-latency, low-memory footprint, and scalable AI performance that delivers tangible value in dynamic, real-world scenarios
- Design and deploy state-of-the-art model serving architectures that deliver high throughput and low latency while optimizing memory usage
- Build, run, and monitor controlled inference tests in both simulated and live production environments
- Track key performance indicators such as response latency, throughput, memory consumption, and error rates
- Document iterative results and compare outcomes against established benchmarks
- Identify and prepare high-quality test datasets and simulation scenarios tailored to real-world deployment challenges
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
- Deep understanding of modern model serving architectures and inference optimization techniques is required
- 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
- Demonstrated ability to apply empirical research to overcome challenges in model serving
- 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.
Benefits:
- Health insurance
- Work from anywhere
- Professional development opportunities
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