Customer Success Engineer

DataRobot delivers AI that maximizes impact and minimizes business risk. Our platform and applications integrate into core business processes so teams can develop, deliver, and govern AI at scale. DataRobot empowers practitioners to deliver predictive and generative AI, and enables leaders to secure their AI assets. Organizations worldwide rely on DataRobot for AI that makes sense for their business today and in the future. The Customer Success Engineer is a post-sales technical expert responsible for driving adoption, consumption, and measurable outcomes from deployed GenAI applications. Customer Success Engineers serve as the technical bridge between developers and DataRobot s platform, ensuring customers maximize value from using DataRobot. They work closely with Account Owner, Engagement Directors, and Professional Services teams to accelerate time-to-value, support expansion motions, and reduce churn risk through continuous enablement and use case optimization.

Key Responsibilities

• 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

Desired Candidate Profile

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 3-5+ 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