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 the AI model team to design and optimize model serving architectures and inference pipelines for edge and on-device deployment. Drive low-latency, high-throughput inference on resource-constrained devices, develop robust testing and benchmarking, and collaborate with cross-functional teams to push the boundaries of efficient AI deployment across mobile and edge platforms.

Location: Zurich

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 KPIs such as latency, throughput, memory consumption, and error rates, with emphasis on resource-constrained devices.
•Identify and prepare high-quality test datasets and simulation scenarios tailored to real-world deployment challenges on low-resource devices. Set measurable criteria to evaluate performance, latency, and memory utilization.
•Analyze computational efficiency and diagnose bottlenecks in the serving pipeline by monitoring processing and memory metrics. Address issues like suboptimal batch processing, network delays, and high memory usage.
•Work with cross-functional teams to integrate optimized serving and inference frameworks into production pipelines designed for edge and on-device applications. Define success metrics and ensure continuous monitoring and iterative 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 A* 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 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 for low-latency, high-throughput performance and efficient memory management in diverse deployment scenarios.
•Must have strong expertise in writing GPU kernels for mobile devices (i.e., smartphones) and a deep understanding of model serving frameworks and engines. Practical experience in developing and deploying end-to-end inference pipelines for resource-constrained devices.

Experience:FintechAIEdge computingMobile computing

Education:Bachelor's

Skills:CommunicationProblem-solvingTeam collaboration

Languages:English

Tech Stack:Metal Shading Language (MSL)GPU kernelsMobile GPUsModel servingInference optimizationEdge computingLatency optimizationTensor 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)

✓ 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: Zurich

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 KPIs such as latency, throughput, memory consumption, and error rates, with emphasis on resource-constrained devices.
•Identify and prepare high-quality test datasets and simulation scenarios tailored to real-world deployment challenges on low-resource devices. Set measurable criteria to evaluate performance, latency, and memory utilization.
•Analyze computational efficiency and diagnose bottlenecks in the serving pipeline by monitoring processing and memory metrics. Address issues like suboptimal batch processing, network delays, and high memory usage.
•Work with cross-functional teams to integrate optimized serving and inference frameworks into production pipelines designed for edge and on-device applications. Define success metrics and ensure continuous monitoring and iterative 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 A* 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 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 for low-latency, high-throughput performance and efficient memory management in diverse deployment scenarios.
•Must have strong expertise in writing GPU kernels for mobile devices (i.e., smartphones) and a deep understanding of model serving frameworks and engines. Practical experience in developing and deploying end-to-end inference pipelines for resource-constrained devices.

Experience:FintechAIEdge computingMobile computing

Education:Bachelor's

Skills:CommunicationProblem-solvingTeam collaboration

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