Customer Success Manager - Enterprise

HumanSignal•San Francisco, CA

17h•$127,000 - $152,000

About The Position

The future of AI — whether in training or evaluation, classical ML or agentic workflows — starts with high-quality data. At HumanSignal, we’re building the platform that powers the creation, curation, and evaluation of that data. From fine-tuning foundation models to validating agent behaviors in production, our tools are used by leading AI teams to ensure models are grounded in real-world signal, not noise. Our open-source product, Label Studio, has become the de facto standard for labeling and evaluating data across modalities — from text and images to time series and agents-in-environments. With over 250,000 users and hundreds of millions of labeled samples, it’s the most widely adopted OSS solution for teams working on building AI systems. Label Studio Enterprise builds on that traction with the security, collaboration, and scalability features needed to support mission-critical AI pipelines — powering everything from model training datasets to eval test sets to continuous feedback loops.We started before foundation models were mainstream, and we’re doubling down now that AI is eating the world. If you're excited to help leading AI teams build smarter, more accurate systems — we’d love to talk. About the Opportunity: We’re looking for a technically-minded Customer Success Manager who has worked in or closely with data science and ML teams - especially those who have annotated data themselves or supported others who have. You’ll help our enterprise customers operationalize labeling workflows, integrate Label Studio into ML pipelines, and unlock greater model performance through better training data. As an early member of the Customer Success team, you’ll play a critical role in shaping our engagement model, helping customers scale, and influencing product direction based on hands-on technical experience.

Requirements

5+ years in customer-facing technical roles, such as Customer Success, Solutions Engineering, or Technical Account Management, with at least 3 years managing customer relationships.
Familiarity with modern ML/AI workflows, data collection, including labeling, model training, quality assurance, and productionization. Experience aligning with strategic focus of Head of Engineering / Head of AI personas a big plus.
Hands-on experience working with data labeling or annotation platforms, and an understanding of how they tie into ML workflows.
Direct experience as a data scientist, ML engineer, or MLOps practitioner - or in a customer-facing role serving that audience.
Strong understanding of AI/ML organizational dynamics - especially pain points and goals of technical leaders like Heads of AI, Data Science, or Engineering.
Experience working in B2B SaaS, ideally at AI/ML companies or startups serving data science teams.

Responsibilities

Own strategic customer relationships across onboarding, adoption, expansion, and renewal.
Partner deeply with ML and data science teams to help them integrate Label Studio into their data pipelines and labeling operations.
Drive business value by identifying ways to reduce labeling cost and increase model performance through efficient workflows and better quality control, making usage recommendations aligned with customer business goals and strategic priorities.
Support usage expansion by aligning with customer technical goals, helping to operationalize labeling workflows as part of their standard practice, and advocating internally for required feature enhancements.
Establish yourself as a trusted technical advisor, providing best practices on annotation project setup, ontology design, human-in-the-loop processes, and data curation strategies.