職務内容

Career Opportunity for a MLOps Engineer in Japan!

 

■ MLOps Engineer

 

■ Company Overview

A research-driven organization at the intersection of AI and robotics, focused on advancing next-generation intelligent systems through cutting-edge research and real-world applications.

 

■ Your Role and Responsibilities 

● Design, implement, and maintain large-scale ML pipelines and optimize model performance for training on massive robot datasets

● Design, deploy, and maintain distributed training clusters to reduce model development cycles

● Collaborate closely with VLA researchers to capture evolving ML infrastructure, data pre-processing, training, monitoring, evaluation, and deployment requirements and continuously improve ML pipeline through analysis and experimentation.

● Optimize ML infrastructure and pipeline for cost, performance, and reliability

● Design, develop, and maintain MLOps tools and platforms to ensure VLA researchers can efficiently visualize and analyze the performance

 

■ Experience and Qualifications

● Master’s degree in Computer Science, Engineering, or a related field (or equivalent practical experience).

● 5+ years of professional experience as a software engineer in MLOps engineering, machine learning, or robotics.

● Experience in deploying distributed systems to popular cloud services such as AWS, GCP, Azure.

● Experience in orchestration tools such as Airflow, Dagster, or Kedro.

● High proficiency in PyTorch or JAX.

 

■ Additional Preferred Qualifications

● Experience with training and fine-tuning techniques for RL, VLM, and VLA models, including distillation, supervised fine-tuning, and policy optimization.

● Experience building ETL/ELT pipelines for data processing.

● Experience in hyper-parameter optimization.

● Experience in distributed training frameworks and cluster management for deep neural network training.
● High proficiency in SQL.

 

■ Good Reasons to Join

● Work at the cutting edge of AI and robotics, shaping real-world intelligent systems—not just theory

 

■ Work Location

Tokyo, Japan
 

Details will be provided during the meeting.