About Liquid AI

Spun out of MIT CSAIL, we build 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.

The Opportunity

As an ML Scientist focused on post-training, you will improve the capabilities and behavior of Liquid Foundation Models for Japan and the global market. You will own 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.

You will work closely with Liquid's model, research, and infrastructure teams in Japan and at our US headquarters. Signals from real deployments will give you concrete, difficult problems to solve; your work will translate those signals into broader improvements in model quality, reliability, and usefulness.

This is a role for a scientist who builds. Strong ideas matter, but so do careful data work, reliable implementations, and measurable improvements in working models.

What We're Looking For

We need someone who:

Thinks experimentally: You form clear hypotheses, establish strong baselines, design informative ablations, and let evidence guide the next step.

Understands models through data: You can identify gaps in training data and design curation, filtering, generation, and supervision strategies to address them.

Owns the full post-training loop: You are comfortable moving between datasets, training, evaluation, error analysis, and implementation.

Builds with rigor: Your experiments are reproducible, your conclusions are well supported, and successful research can be integrated into shared systems.

Collaborates across boundaries: You communicate clearly with globally distributed research, engineering, and applied teams.

The Work

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.

Desired Experience

Must-have

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.

Nice-to-have

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.