Solutions Architect (Post Sales)

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:

As a Solutions Architect at HumanSignal, you’ll ensure our enterprise customers realize maximum value from their Label Studio Enterprise investment. Partnering closely with CSMs, you’ll deliver an exceptional customer experience by guiding customers through onboarding, workflow design, integration, and ongoing optimization - serving as their trusted technical advisor in the post-sale journey.

This role goes beyond a traditional SA position. Our customers depend on us to help them design and scale annotation pipelines, improve data quality, and connect Label Studio with their ML lifecycle (ingestion > labeling > training > evaluation). You’ll work directly with ML Engineers, Data Scientists, and MLOps teams to architect real-world solutions that fit into production workflows.

To succeed here, you should have hands-on experience with ML or data labeling workflows, whether through building ML pipelines that rely on labeled data, working with annotation tools or HITL systems, or supporting data-centric ML development. Strong customer-facing skills and ownership of technical onboarding are essential. If your background is primarily pure software engineering, generic SaaS presales, or research without production ML exposure, this role likely won’t be a fit.

This high-impact, visible role puts you at the center of customer adoption and success. As an early member of our Customer Success team, you’ll help shape our engagement model and influence our product direction through direct technical insights.

What You’ll Do:

• Drive technical onboarding for enterprise customers, guiding them through installation, secure configuration, and best-practice deployment of Label Studio Enterprise across cloud, on-prem, or hybrid environments.
• Advise on and help architect integrations between Label Studio and customer AI/ML workflows, pipelines, storage, and enterprise systems.
• Develop and implement custom solutions (scripts, plug-ins, and APIs) to extend Label Studio functionality based on unique customer requirements.
• Troubleshoot and resolve advanced technical issues, serving as an escalation point for Customer Success Managers and collaborating with Product, Engineering, and Support as needed.
• Deliver enablement, workshops, and technical documentation to empower users and drive self-sufficiency with the platform.
• Act as a trusted technical advisor, building strong relationships with customer engineering, data, and AI/ML teams to support adoption, expansion, and long-term value.
• Surface customer feedback and advocate for product improvements by sharing learnings with Product and Engineering.
• Contribute to internal best practices and reusable solution assets for future customer engagements.

What You'll Bring:

• 3+ years in a customer-facing technical role (e.g., Solutions Architect, Sales Engineer, Professional Services Engineer, or similar) for a highly technical product, ideally enterprise SaaS or ML/AI platforms.
• Direct experience building a data labeling pipeline (e.g. data annotation, labeling workflows, and/or the ML lifecycle) and the ability to effectively communicate these concepts to audiences.
• Experience integrating SaaS platforms with ML pipelines; has built & deployed ML models to production and/or done engineering work to prepare datas