1 day ago

Member of Technical Staff - ML Scientist, Japanese Multimodal

✓ 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

Improve Liquid Foundation Models for Japan and global markets by owning the full post-training experimental loop. You’ll identify weaknesses, design data and training strategies, run controlled experiments and ablations, and develop evaluations and error analysis to uncover capability and reliability gaps. Collaborating with model, research, and infrastructure teams across Japan and the US, you’ll turn successful ideas into reusable checkpoints, datasets, and scalable training/evaluation pipelines.

Location: Tokyo

Workplace: Hybrid

Employment Type: Full time

Job Function: Research & Scientific (R&D)

Seniority: Mid level

Key Responsibilities

•Design and execute post-training strategies for language and multimodal models (supervised fine-tuning, preference optimization, reinforcement learning, distillation).
•Build and curate high-quality training data (human, synthetic, and model-generated signals), emphasizing Japanese-language and domain-specific capabilities.
•Develop evaluations to expose meaningful capability and reliability gaps across Japanese and global use cases.
•Conduct systematic error analysis and use findings to improve data mixtures, objectives, training methods, and model behavior.
•Develop reliable training and evaluation pipelines and contribute methods, tooling, datasets, and findings to accelerate post-training across Liquid AI.

Travel: Medium travel

Key Requirements

•Hands-on experience with post-training modern language or multimodal models.
•Strong understanding of machine learning fundamentals plus current post-training and reinforcement learning methods.
•Solid engineering skills and proficiency with the open-source ML ecosystem.
•Experience designing and running rigorous experiments (baselines, ablations, systematic error analysis).
•Experience building, curating, or assessing training and evaluation data at meaningful scale, and the ability to deliver measurable model improvements.

Experience:Post-trainingMachine learningFoundation modelsMultimodal modelsReinforcement learning

Skills:Scientific judgmentExperimental thinkingCollaborationReproducibilityTechnical communication

Languages:EnglishJapanese

Company Brief

Liquid AI

Builds AI infrastructure and tooling to enable real-time, distributed machine learning and orchestration across edge and cloud environments, simplifying deployment and management of intelligent applications.

Industry: AI & Machine Learning

Website

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Liquid AI

1 day ago

Member of Technical Staff - ML Scientist, Japanese Multimodal

✓ 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: Tokyo

Workplace: Hybrid

Employment Type: Full time

Job Function: Research & Scientific (R&D)

Seniority: Mid level

Key Responsibilities

•Design and execute post-training strategies for language and multimodal models (supervised fine-tuning, preference optimization, reinforcement learning, distillation).
•Build and curate high-quality training data (human, synthetic, and model-generated signals), emphasizing Japanese-language and domain-specific capabilities.
•Develop evaluations to expose meaningful capability and reliability gaps across Japanese and global use cases.
•Conduct systematic error analysis and use findings to improve data mixtures, objectives, training methods, and model behavior.
•Develop reliable training and evaluation pipelines and contribute methods, tooling, datasets, and findings to accelerate post-training across Liquid AI.

Travel: Medium travel

Key Requirements

•Hands-on experience with post-training modern language or multimodal models.
•Strong understanding of machine learning fundamentals plus current post-training and reinforcement learning methods.
•Solid engineering skills and proficiency with the open-source ML ecosystem.
•Experience designing and running rigorous experiments (baselines, ablations, systematic error analysis).
•Experience building, curating, or assessing training and evaluation data at meaningful scale, and the ability to deliver measurable model improvements.

Experience:Post-trainingMachine learningFoundation modelsMultimodal modelsReinforcement learning

Skills:Scientific judgmentExperimental thinkingCollaborationReproducibilityTechnical communication

Languages:EnglishJapanese

Company Brief

Liquid AI

Industry: AI & Machine Learning

Website