Key Facts

Remote From:

Oregon (USA)

Full time

Senior (5-10 years)

127 - 190K yearly

English

Hard Skills

Customer Acquisition Management Customer Success Management Data Science MLOps (Machine Learning Operations) Human-In-The-Loop Model Validation Multi-Agent Systems Ontologies Customer Success Management Systems Engineering Machine Learning Data Curation Sound Studio Cross-Functional Collaboration Customer Success Management Process Design Project Management Stakeholder Communications Employee Onboarding

Other Skills

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Quality Assurance
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Communication
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Time Management
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Strategic Thinking
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Relationship Building
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Problem Solving

Roles & Responsibilities

5+ years in customer-facing technical roles (e.g., 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
Hands-on experience with data labeling/annotation platforms and understanding 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

Requirements:

Own strategic customer relationships across onboarding, adoption, expansion, and renewal
Partner with ML and data science teams to integrate Label Studio into data pipelines and labeling operations
Drive business value by reducing labeling costs and increasing model performance through efficient workflows and quality control
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

HumanSignal

About HumanSignal

HumanSignal (formerly known as Heartex) enables data science teams to build AI models with their company DNA. With the emergence of generative AI, it’s more important than ever to build highly differentiated models by guiding foundation models with proprietary data and human feedback. Creators of Label Studio, the most popular open source data labeling platform, HumanSignal enables data scientists to develop high quality datasets and workflows for model training, fine tuning and continuous validation. Today, the Label Studio open source community has more than 250,000 users who have collectively annotated more than 100 million pieces of data. Label Studio Enterprise is available as a cloud service with enhanced security, automation, quality review workflows, and performance reporting, used by leading data science teams including Bombora, Geberit, Outreach, Wyze, and Zendesk.

Company type: Scaleup

Founded: 2018

Company size: 51 - 200

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Job description

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.

What You’ll Do:

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.

What You'll Bring:

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.

What Success Looks Like:

Within 1 month, you'll:

Ramp on our product & company value proposition such that you can effectively articulate it to customers.
Meet key enterprise customers and begin gathering success criteria and feedback.
Identify gaps between the current status and the client’s desired outcome and how those gaps could be bridged.
Partner with Sales, Technical, and Product/Marketing peers to craft Joint Success Plans for customers.

Within 3 months, you'll:

Deliver high-impact onboarding & adoption plans.
Serve as a proactive advisor on best practices for product usage and share applicable success stories to help customers transform their data labeling practice.
Partner with your champions to evangelize success and achievements to further drive awareness and expansion opportunities within the organization.
Capture product feedback and feature requests, identify the “why” and value behind that information to connect it to customer outcomes.
Track towards key targets for customer satisfaction, renewals, and expansion.

Within 6 months, you'll:

Nurture relationships with strategic accounts across user, technical, and executive personas. Guarantee satisfaction and alignment along every step
Help continue building out customer success functions by drafting best practices, playbooks, and process designs in order for the team to serve a growing book of customers at scale.
Represent the voice of the customer in cross-functional discussions.

Within 1 year you'll:

Become a go-to technical leader for our most strategic accounts.
Be proactive in identifying future obstacles in terms of larger customers' adoption and usage, and identify opportunities to expand their investment.
Keep scaling customer success initiatives based on customer and usage growth, contributing to the team’s evolution as we scale.

It is an exciting time at HumanSignal; we are a growing startup and are constantly evolving. While we have put a lot of thought into your first and most important initiatives, we will develop these goals together. We're always learning and growing, so like us this role will evolve and expand. If this opportunity sounds exciting to you and you want to be part of our journey, we want to hear from you!

Why Join HumanSignal?

We’re building the future of data-centric AI - an area with growing momentum and industry backing.
Our product is already beloved by a thriving open source and enterprise community.
You’ll work alongside experienced, mission-driven teammates who care deeply about quality, usability, and impact.
We’re a startup with strong funding and massive room for growth.

At HumanSignal

While our community and customers enjoy our products, we understand it is our team who make that possible. That is why we want to support you in doing your best work. To explore more about our team please visit the about our team page.

We welcome applicants from all backgrounds, even if you do not meet 100% of the listed criteria. Please include a resume or LinkedIn profile, a cover letter expanding on your background and interest in HumanSignal is appreciated but not required.

HumanSignal is proud to be an Equal Opportunity Employer. We are committed to building an organization that welcomes diverse backgrounds and lifestyles. Our goal is to create an inclusive work environment that is equitable and where everyone feels they belong. We foster open and transparent communication and a workplace where discrimination and harassment are not tolerated. If you need any accommodations or assistance to make your interviews more accessible please do not hesitate to reach out to us.

We do not discriminate against employees or applicants based on gender identity or expression, sexual orientation, religion, age, race, military/veteran status, citizenship, pregnancy status, or any other differences.

We are hiring for this role across North America. Base Salary is targeted for $127,000 – $152,000 USD (based on experience and skills). This role also qualifies for variable compensation; anticipated On-Target Earnings are $158,000 – $190,000 USD. Final offer details are determined by several factors including candidate experience, expertise as well as applicable industry knowledge, and may vary from the pay ranges listed above. We also offer stock options, comprehensive health benefits, and a strong team culture rooted in transparency and collaboration. Join us!

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