AI Research Engineer (Kernel & Inference Optimization) - 100% Remote Worldwide

✓ Verified Job

Canonical indexed version, validated from employer's careers page.

Source: Company careers pageValidated by: Fursa AI

Last checked: 3 hours agoStatus: Live

Job Summary

Join a global, remote AI model team focused on designing and optimizing state-of-the-art model serving and inference architectures. You will develop low-latency, memory-efficient inference pipelines for resource-constrained devices and edge platforms, pushing the boundaries of kernel and inference optimization across diverse environments.

Location: Italy, Argentina, Colombia, Brazil, Uruguay, India, Bangladesh, Pakistan, Vietnam, Taiwan, Thailand, United Kingdom, Switzerland, Spain, United Arab Emirates, Bulgaria, Czech Republic, Serbia, Denmark, Estonia, Greece, Georgia, Budapest, Ireland, Malta, Norway, Amsterdam, Poland, Portugal, Romania, Sweden, Belgium, Israel, Cyprus

Workplace: Remote

Employment Type: Full time

Job Function: Research & Scientific (R&D)

Key Responsibilities

•Design and deploy state-of-the-art model serving architectures that deliver high throughput and low latency while optimizing memory usage. Ensure these pipelines run efficiently across diverse environments, including resource-constrained devices and edge platforms.
•Build, run, and monitor controlled inference tests in both simulated and live production environments. Track key performance indicators such as latency, throughput, memory consumption, and error rates.
•Identify and prepare high-quality test datasets and simulation scenarios for real-world deployment challenges on low-resource devices. Set measurable criteria to evaluate performance under various conditions.
•Analyze computational efficiency and diagnose bottlenecks in the serving pipeline by monitoring processing and memory metrics. Address issues to optimize scalability and reliability on constrained systems.
•Work with cross-functional teams to integrate optimized serving and inference frameworks into production pipelines for edge and on-device applications, defining success metrics and ensuring ongoing refinements.

Key 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 top conferences).
•Must have knowledge of Metal Shading Language (MSL). You should be comfortable writing custom compute shaders from scratch.
•Proven experience in low-level kernel optimizations and inference optimization on mobile devices. Contributions should have led to measurable improvements in latency, throughput, and memory footprint.
•A deep understanding of modern model serving architectures and inference optimization techniques for low-latency, high-throughput deployment across resource-constrained environments.
•Must have strong expertise in writing GPU kernels for mobile devices (smartphones) and a deep understanding of model serving frameworks and end-to-end inference pipelines for on-device deployment.

Experience:AIMLEdge computingInferenceKernel optimization

Education:Bachelor's

Skills:MSLGPU kernelsModel servingInferenceLow-level optimizationEdge deploymentLatency optimizationMemory managementTensor ParallelismPipeline ParallelismExpert ParallelismDiffusion ModelsVision TransformersPruningQuantizationFlash attentionKV CacheSpeculative Decoding

Languages:English

Tech Stack:Metal Shading LanguageGPU kernelsMobileModel servingInferenceTensor ParallelismPipeline ParallelismExpert ParallelismDiffusion ModelsVision TransformersPruningQuantizationFlash attentionKV CacheSpeculative Decoding

Company Brief

Tether.io

Builds tools and services to help companies hire, onboard, and manage remote or distributed teams across borders, focusing on payroll, compliance, and global employment workflows.

Industry: HR Tech

Website

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Tether.io

2 months ago

AI Research Engineer (Kernel & Inference Optimization) - 100% Remote Worldwide

✓ Verified Job

Canonical indexed version, validated from employer's careers page.

Source: Company careers pageValidated by: Fursa AI

Last checked: 3 hours agoStatus: Live

Job Summary

Workplace: Remote

Employment Type: Full time

Job Function: Research & Scientific (R&D)

Key Responsibilities

•Design and deploy state-of-the-art model serving architectures that deliver high throughput and low latency while optimizing memory usage. Ensure these pipelines run efficiently across diverse environments, including resource-constrained devices and edge platforms.
•Build, run, and monitor controlled inference tests in both simulated and live production environments. Track key performance indicators such as latency, throughput, memory consumption, and error rates.
•Identify and prepare high-quality test datasets and simulation scenarios for real-world deployment challenges on low-resource devices. Set measurable criteria to evaluate performance under various conditions.
•Analyze computational efficiency and diagnose bottlenecks in the serving pipeline by monitoring processing and memory metrics. Address issues to optimize scalability and reliability on constrained systems.
•Work with cross-functional teams to integrate optimized serving and inference frameworks into production pipelines for edge and on-device applications, defining success metrics and ensuring ongoing refinements.

Key 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 top conferences).
•Must have knowledge of Metal Shading Language (MSL). You should be comfortable writing custom compute shaders from scratch.
•Proven experience in low-level kernel optimizations and inference optimization on mobile devices. Contributions should have led to measurable improvements in latency, throughput, and memory footprint.
•A deep understanding of modern model serving architectures and inference optimization techniques for low-latency, high-throughput deployment across resource-constrained environments.
•Must have strong expertise in writing GPU kernels for mobile devices (smartphones) and a deep understanding of model serving frameworks and end-to-end inference pipelines for on-device deployment.

Experience:AIMLEdge computingInferenceKernel optimization

Education:Bachelor's

Languages:English

Company Brief

Tether.io

Builds tools and services to help companies hire, onboard, and manage remote or distributed teams across borders, focusing on payroll, compliance, and global employment workflows.

Industry: HR Tech

Website