AI Research Engineer (Model Compression & Quantization)

✓ 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

Research and develop model compression techniques for multimodal AI (LLMs and VLMs) to reduce footprint and latency. Build robust compression pipelines using quantization, knowledge distillation, and pruning; evaluate trade-offs between size, speed, and accuracy; stay at the cutting edge with new techniques and publish findings. Remote role with a global, async team and edge deployment focus.

Location: Brazil

Workplace: Remote

Employment Type: Full time

Job Function: Research & Scientific (R&D)

Key Responsibilities

•Apply low-bit quantization to reduce model size and inference latency for generative AI models while maintaining accuracy and output quality.
•Leverage knowledge distillation to transfer capabilities from larger teacher models to smaller student models for efficient multimodal reasoning across text, image, and audio inputs.
•Implement pruning techniques to remove redundant parameters and attention heads, reducing computational overhead without sacrificing task performance.
•Analyze trade-offs between model efficiency (size, latency, memory) and accuracy across quantization, distillation, and pruning methods; propose improvements based on empirical findings.
•Research and apply mixed-precision quantization and other advanced compression strategies to optimize the accuracy–performance balance.

Key Requirements

•A degree in Computer Science or related field, ideally PhD in NLP/Machine Learning with publications in top conferences
•Experience with PyTorch or equivalent deep learning frameworks
•Hands-on experience with model quantization (QAT and PTQ)
•Experience with knowledge distillation for compressing large models
•Experience with model pruning to reduce parameters and computation

Experience:FintechAI

Education:Bachelor's

Skills:CommunicationCollaborationResearch

Tech Stack:PyTorchC++QuantizationQATPTQ

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 (Model Compression & Quantization)

✓ 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: Brazil

Workplace: Remote

Employment Type: Full time

Job Function: Research & Scientific (R&D)

Key Responsibilities

•Apply low-bit quantization to reduce model size and inference latency for generative AI models while maintaining accuracy and output quality.
•Leverage knowledge distillation to transfer capabilities from larger teacher models to smaller student models for efficient multimodal reasoning across text, image, and audio inputs.
•Implement pruning techniques to remove redundant parameters and attention heads, reducing computational overhead without sacrificing task performance.
•Analyze trade-offs between model efficiency (size, latency, memory) and accuracy across quantization, distillation, and pruning methods; propose improvements based on empirical findings.
•Research and apply mixed-precision quantization and other advanced compression strategies to optimize the accuracy–performance balance.

Key Requirements

•A degree in Computer Science or related field, ideally PhD in NLP/Machine Learning with publications in top conferences
•Experience with PyTorch or equivalent deep learning frameworks
•Hands-on experience with model quantization (QAT and PTQ)
•Experience with knowledge distillation for compressing large models
•Experience with model pruning to reduce parameters and computation

Experience:FintechAI

Education:Bachelor's

Skills:CommunicationCollaborationResearch

Tech Stack:PyTorchC++QuantizationQATPTQ

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