Customer Success Engineer

DataRobot

Customer Success Engineer maximizing AI platform adoption and success for DataRobot clients. Acting as a technical bridge, ensuring customers achieve value from AI applications within a remote capacity.

Posted 5/19/2026full-timeRemote • 🇯🇵 JapanMid-LevelSeniorWebsite

Tech Stack

Tools & technologies

AWSAzureCloudGoogle Cloud Platform

About the role

Key responsibilities & impact

Accelerate Onboarding & Initial Application Adoption: Guide customers through first-use milestones by enabling key personas, resolving blockers, and ensuring consumption of initial apps deployed during onboarding.
Drive Ongoing Consumption: Monitor usage, identify underutilized apps / stalled users, and engage with customers to increase activation and business impact.
Customer Health Monitoring: Actively track product usage, satisfaction, and success milestones to surface risk early and coordinate mitigation plans.
Technical Advocacy & Solution Feedback: Act as the voice of the customer to DataRobot product and engineering teams, channeling technical requirements, gaps, and enhancement requests.
Accelerate Initial Group Learning Adoption: Facilitate onboarding workshops and training sessions for multiple user groups, enabling key personas to reach first-use milestones and overcome common blockers.
Technical Enablement & Training: Deliver targeted, scalable enablement sessions and create reusable knowledge-sharing materials designed for diverse audiences across accounts.
Use Case Value Realization: Collaborate with Engagement Directors to ensure learning initiatives align with business goals and capture feedback and outcomes for executive reviews.

Requirements

What you’ll need

5+ years of experience in technical customer-facing roles (e.g., Solution Engineer, AI/ML Engineer, Technical CSM, App Developer) in SaaS or enterprise software
Bachelor's degree in a technical, business, or related field (or equivalent practical experience); advanced degree a plus
Familiarity with AI platforms, application lifecycle management, or data-centric solution delivery
AI Engineering to include GenAI application development, prompt engineering, and knowledge of LLMs
Strong presentation and communication skills, with the ability to engage both business users and technical stakeholders
Proven ability to translate complex technical functionality into measurable business outcomes
Experience in supporting product adoption, managing customer success plans, and driving technical consumption
Extensive experience with the end-to-end machine learning lifecycle, including feature engineering, model training, evaluating accuracy and insights, and deploying models for inference
Understanding of GenAI application architectures and LLM implementations
Familiarity with cloud infrastructure (AWS/Azure/GCP) and deployment patterns
Comfortable reading code/logs to diagnose technical issues
Professional proficiency in both Japanese and English, encompassing both written and verbal communication skills.

Benefits

Comp & perks

Medical, Dental & Vision Insurance
Flexible Time Off Program
Paid Holidays
Paid Parental Leave
Global Employee Assistance Program (EAP) and more!

ATS Keywords

✓ Tailor your resume

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

AI EngineeringGenAI application developmentprompt engineeringknowledge of LLMsmachine learning lifecyclefeature engineeringmodel trainingevaluating accuracydeploying modelsapplication lifecycle management

Soft Skills

presentation skillscommunication skillscustomer advocacyproblem-solvingcollaborationtraining facilitationengagement with stakeholderstranslating technical functionalitycustomer success managementrisk mitigation