Operations Specialist

Operations SpecialistThe 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. HumanSignal 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 environmentsthis 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. You will: 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 Ideally, you'd have: 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-solvingyou 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 Nice to haves: Experience with machine learning workflows or understanding of how training data impacts model performance Familiarity with Python or other scripting languages for automation Prior experience at a startup, tech company, or in data operations roles Knowledge of data annotation, labeling platforms, or quality control processes Experience supporting technical customers or working on customer-facing delivery teams About HumanSignal: At HumanSignal, we're building the infrastructure for the next generation of AI. Our Label Studio platform powers data operations for leading organizations worldwide, while our Data Creation Laboratories manufacture the purpose-built datasets that breakthrough AI applications require. We believe the next frontiers in AI won't be unlocked by scraping what's left on the webthey'll be built on human-created data that captures the complexity of how systems need to see, hear, reason, and react. Through controlled environments and operational excellence, we're enabling researchers and enterprises to innovate without being constrained by data availability. We work with frontier AI labs, Fortune 500 enterprises, and government agencies who are pushing the boundaries of what's possible with AI. Join us in building the data that will build the future. We are proud to be an inclusive and equal opportunity workplace. We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability status, gender identity, or Veteran status. At HumanSignal we pay based on regional compensation market rate ranges across the globe. We are hiring for this role across North and South America as well as Europe. The base cash compensation range is $45,000 to $85,000 USD. These ranges are provided by market data and are in good faith. The 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.