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AI Research Engineer – Kernel, Inference Optimization

AI Research ScientistMachine Learning EngineerFull TimeRemoteSeniorTeam 11-50Since 2014H1B No SponsorCompany SiteLinkedIn

Location

Brazil

Posted

7 days ago

Salary

0

Seniority

Senior

Postgraduate DegreeEnglishFlash

Job Description

AI Research Engineer – Kernel, Inference Optimization

Tether.to

• 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. • Adopt a hands-on, research-driven approach to develop, test, and implement novel serving strategies and inference algorithms. • Engineer robust inference pipelines, establishing comprehensive performance metrics, and identifying and resolving 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.

Job 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.
  • 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

  • Our team is a global talent powerhouse, working remotely from every corner of the world.

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