Member of Technical Staff - ML Scientist, Japanese Multimodal

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. 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.

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

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; A track record of producing useful research artifacts, such as strong models, datasets, open-source systems, or technical reports; Reading proficiency in Japanese

Benefits

High-impact model work: Improve the capabilities and behavior of foundation models used in real products and deployments; Direct access to core teams: Collaborate closely with the researchers and engineers building Liquid's models and training systems; Ownership in a growing market: Help define our post-training approach in Japan at a time of strong customer demand and rapid growth; Supportive & growth-driven culture: Thrive in a collaborative, feedback-rich environment that prioritizes continuous learning, mentorship, and personal career development; Flexibility: Primarily remote work, flexible working hours, and unlimited paid time off; Competitive rewards: Competitive salary, equity in a unicorn-stage company, and standard benefits for employees in Japan