Member of Technical Staff - Applied ML, Japanese Multimodal

As an Applied ML Engineer on the Japan team, you will own the technical path from a customer's problem to a deployed AI solution. You will work directly with technical teams at leading companies in Japan and collaborate closely with Liquid's research, model, inference, and product teams in Japan and at our US headquarters. The center of gravity for this role is deployment: making models perform reliably in the environments where customers actually need them.

Responsibilities

Own applied ML projects for customers in Japan, from technical discovery and scoping through production deployment; Integrate, profile, and optimize model inference to meet concrete requirements for latency, throughput, memory, power, cost, and reliability; Build the surrounding software needed to turn a model into a robust product capability, including data pipelines, evaluation systems, serving components, and reference implementations; Fine-tune or post-train models when needed using techniques such as supervised fine-tuning, parameter-efficient fine-tuning, and preference optimization; Design task-specific evaluations, conduct systematic error analysis, and iterate across data, models, inference, and system design; Work directly with customer engineering teams during design, integration, testing, and rollout, including occasional on-site work; Turn lessons from individual deployments into reusable tooling and feedback that improves Liquid's models, inference stack, documentation, and product roadmap.

Requirements

Strong engineering skills and experience building, testing, and shipping production-quality ML systems; Hands-on experience deploying modern language models, multimodal models, or other deep learning systems beyond a notebook or API proof of concept; Experience with model serving, performance profiling, or inference optimization, and the judgment to balance model quality with system constraints; Proficiency with the open-source ML ecosystem; Experience designing evaluations, analyzing model failures, and using the results to drive measurable improvements; Comfort leading technical discussions with customers and translating ambiguous requirements into shipped systems; Professional proficiency in English, including the ability to collaborate on complex technical work with global teams; Experience leveraging agents to amplify your own work; Owns outcomes end to end: You take responsibility from technical discovery through implementation, validation, optimization, and deployment; Enjoys technical customer work: You can explore a problem with a customer's engineers, challenge assumptions constructively, and turn an ambiguous need into a sound technical plan; Builds for the real environment: You treat latency, memory, compute, privacy, reliability, and maintainability as part of the ML problem; Works with rigor: You move quickly while using strong baselines, profiling, careful evaluation, and disciplined error analysis to decide what works; Collaborates across boundaries: You communicate clearly across customers, the Japan team, and globally distributed research and engineering teams.

Nice to have

Working proficiency in Japanese; Experience with LLM post-training methods; Experience with inference and deployment frameworks such as vLLM, SGLang, llama.cpp, ONNX Runtime, MLX; Familiarity with quantization, hardware-aware optimization, or deployment on mobile, embedded, automotive, or other edge platforms; Experience with multimodal systems involving text, vision, audio, or sensor data; Experience delivering ML systems for enterprise or regulated environments.

Benefits

High-impact work; Distinctive technical problems; Direct access to core teams; Ownership in a growing market; Supportive & growth-driven culture; 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.