Operations Specialist

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The future of AI — whether in training or evaluation, classical ML or agentic workflows — starts with high-quality data. At Human Signal, 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 is 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 evaluation 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.

Human Signal is seeking an Operations Specialist to support our delivery teams as we scale our Label Studio platform and Data Creation Laboratory operations. In this role, you'll be the critical support system for delivery leads managing complex data projects for frontier AI labs and enterprise customers. You'll bridge technical operations and project execution, ensuring seamless delivery of the purpose-built datasets that power breakthrough AI applications.

Our Data Creation Laboratories manufacture training data from scratch in controlled environments—this isn't traditional data labeling. The technical complexity of what we deliver demands operators who understand data architecture, can troubleshoot pipeline issues, and think systematically about how data flows from creation to customer delivery. If you have an engineering mindset and want to be hands‑on in the most exciting space in AI, this role offers real impact.

• Support delivery leads in executing high-stakes data projects from kickoff through delivery.
• Monitor data pipelines and quality workflows, identifying bottlenecks and troubleshooting issues before they impact customers.
• Coordinate across technical teams including engineering, quality control, and laboratory operations to ensure on‑time delivery.
• Help maintain project tracking systems, documentation, and delivery reporting.
• Participate in technical discussions about data architecture, delivery workflows, and pipeline optimization.
• Assist with onboarding new projects by setting up infrastructure, configuring tools, and ensuring technical readiness.
• Support operational analytics by pulling data, building dashboards, and surfacing insights that improve delivery performance.
• Act as a technical liaison between delivery teams and customers, helping translate requirements and address technical questions.
• Contribute to process improvements that make data creation and delivery faster, more reliable, and higher quality.

• Bachelor's degree in Computer Science, Engineering, Data Science, or a related technical field.
• 1–3 years of experience in operations, technical program management, or engineering roles.
• Strong technical foundation with understanding of data pipelines, APIs, and system architecture.
• Proficiency with SQL and ability to work with databases to analyze operational data.
• Experience with project management tools and workflow systems (Jira, Asana, Notion, etc.).
• Analytical mindset with attention to detail and ability to spot issues before they escalate.
• Excellent communication skills and comfort working cross‑functionally with technical and non‑technical stakeholders.
• Bias toward action and problem‑solving—you don't wait to be told what to do when you see something broken.
• Comfort in fast‑paced environments where priorities shift and you need to adapt quickly.

• Experience with machine learning…