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About the Role

Improve the capabilities and behavior of Liquid Foundation Models for Japan and the global market by owning the full experimental loop: identifying model weaknesses, developing data and training strategies, running controlled experiments, evaluating results, and turning successful ideas into high-quality checkpoints and reusable methods. This role is for a scientist who builds, with strong ideas, careful data work, reliable implementations, and measurable improvements in working models being highly valued.

Responsibilities

Design and execute post-training strategies for language and multimodal models, including supervised fine-tuning, preference optimization, reinforcement learning, and distillation.
Build and curate high-quality training data using human, synthetic, and model-generated signals, with particular attention to Japanese-language and domain-specific capabilities.
Develop evaluations that expose meaningful capability and reliability gaps across Japanese and global use cases.
Conduct systematic error analysis and use the results to improve data mixtures, objectives, training methods, and model behavior.
Run controlled experiments and ablations, interpret results, and communicate clear recommendations.
Develop reliable, scalable training and evaluation pipelines in collaboration with model infrastructure teams.
Contribute methods, tooling, datasets, and findings that accelerate post-training work across Liquid AI.

Requirements

Hands-on experience post-training modern language or multimodal models.
Strong understanding of machine learning fundamentals and current post-training and RL methods.
Solid engineering skills and proficiency with the open-source ML ecosystem.
Experience designing and running rigorous experiments, including baselines, ablations, and systematic error analysis.
Experience building, curating, or assessing training and evaluation data at a meaningful scale.
Ability to turn research ideas into reliable implementations and measurable model improvements.
Proficiency in English, including the ability to collaborate on complex technical work with global teams.
Experience leveraging agents to amplify your own work.
Experience with preference optimization or reinforcement learning methods for foundation models.
Experience post-training multimodal models involving text, vision, or audio.
Experience developing synthetic data pipelines, reward models, verifiers, or model-based evaluations.
A track record of producing useful research artifacts, such as strong models, datasets, open-source systems, or technical reports.
Reading proficiency in Japanese.

Skills

Machine learning
Post-training methods
Reinforcement learning
Language models
Multimodal models
Supervised fine-tuning
Preference optimization
Distillation
Data curation
Experimental design
Error analysis
Reproducible experiments
Open-source ML ecosystem
Engineering skills
Collaboration
Communication

Location

Japan
Remote

Work Type

Remote
Hybrid

Experience Level

Mid-level
Senior

Benefits

Equity
Unlimited paid time off
Standard benefits for employees in Japan

About the Company

Spun out of MIT CSAIL, Liquid AI builds general-purpose AI systems that run efficiently across deployment targets, from data center accelerators to on-device hardware, ensuring low latency, minimal memory usage, privacy, and reliability.
We partner with enterprises across consumer electronics, automotive, life sciences, and financial services.
We are scaling rapidly and need exceptional people to help us get there.