AI Research Engineer (Kernel & Inference Optimization)

✓ Verified Job

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

Source: Company careers pageValidated by: Fursa AI

Last checked: 22 hours agoStatus: Live

Job Summary

Join a remote-first AI model team to design and optimize kernel-level inference and model serving pipelines for resource-constrained devices and edge platforms. You will develop high-throughput, low-latency deployments across multi-modal architectures, using cutting-edge techniques to push the boundaries of on-device AI performance in a fintech context.

Location: Amsterdam

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 across diverse environments, including edge platforms.
•Build, run, and monitor controlled inference tests in simulated and live production environments, tracking latency, throughput, memory usage, and error rates.
•Identify and prepare high-quality test datasets and simulation scenarios for real-world deployment challenges on low-resource devices.
•Analyze computational efficiency and diagnose bottlenecks in the serving pipeline, addressing latency, throughput, and memory issues to improve scalability.
•Collaborate with cross-functional teams to integrate optimized serving and inference frameworks into production pipelines for edge and on-device applications.

Key Requirements

•A degree in Computer Science or related field.
•Must have knowledge of Metal Shading Language (MSL) and be comfortable writing custom compute shaders from scratch.
•Proven experience in low-level kernel optimizations and inference optimization on mobile devices with 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.
•Strong expertise in writing GPU kernels for mobile devices and integrating end-to-end inference pipelines on resource-constrained devices.

Experience:FintechAI/MLEdge computing

Education:Bachelor's

Skills:Metal Shading LanguageGPU kernelsModel servingInference optimizationEdge deployment

Languages:English

Tech Stack:MetalGPU kernelsOn-device MLInference frameworks

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)

✓ Verified Job

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

Source: Company careers pageValidated by: Fursa AI

Last checked: 22 hours agoStatus: Live

Job Summary

Location: Amsterdam

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 across diverse environments, including edge platforms.
•Build, run, and monitor controlled inference tests in simulated and live production environments, tracking latency, throughput, memory usage, and error rates.
•Identify and prepare high-quality test datasets and simulation scenarios for real-world deployment challenges on low-resource devices.
•Analyze computational efficiency and diagnose bottlenecks in the serving pipeline, addressing latency, throughput, and memory issues to improve scalability.
•Collaborate with cross-functional teams to integrate optimized serving and inference frameworks into production pipelines for edge and on-device applications.

Key Requirements

•A degree in Computer Science or related field.
•Must have knowledge of Metal Shading Language (MSL) and be comfortable writing custom compute shaders from scratch.
•Proven experience in low-level kernel optimizations and inference optimization on mobile devices with 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.
•Strong expertise in writing GPU kernels for mobile devices and integrating end-to-end inference pipelines on resource-constrained devices.

Experience:FintechAI/MLEdge computing

Education:Bachelor's

Skills:Metal Shading LanguageGPU kernelsModel servingInference optimizationEdge deployment

Languages:English

Tech Stack:MetalGPU kernelsOn-device MLInference frameworks

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