About HumanSignal

Real-world data is the competitive edge in AI. HumanSignal is a human data partner for companies building AI models and products. Our customers ship better AI, faster, because we partner with their researchers from real-world data creation to annotation to delivery.

We design and create datasets from scratch, recruit and manage the domain experts who evaluate model output, and run everything through our own platform, Label Studio, the open-source standard for data labeling and evaluation, used by over 1 million practitioners worldwide.

We specialize in the operationally complex: real-world data collection, multimodal pipelines, and multi-step workflows. Advanced ML and AI teams use our enterprise platform to run their own data factories, and our services team to extend their reach where in-house capacity runs out.

If you want to do work that materially shapes how the next generation of AI products gets built, we'd love to talk.

Job Description

We are seeking a talented Support Engineer to join our team and provide exceptional technical support for our industry-leading data labeling software. As a Support Engineer, you will play a crucial role in ensuring the seamless operation of our platform and assisting our clients in resolving any technical issues they encounter. You will work closely with our product and engineering teams to deliver prompt and effective solutions, contributing to the overall success of our product.

As our team scales, this role will play a critical part in advancing our support maturity—improving processes, strengthening our ability to deliver proactive support, and contributing to internal tooling and automation.

Responsibilities:

• Provide technical support to clients via email and support tickets, addressing inquiries related to the installation, configuration, and usage of Label Studio.
• Diagnose and troubleshoot software issues reported by clients, utilizing debugging tools and logs to identify root causes and implement timely resolutions.
• Collaborate closely with Product, Engineering, and Customer Success to drive issue resolution.
• Create internal and external documentation to improve support quality and self-service.
• Proactively identify opportunities for process improvements and contribute to the continuous enhancement of our support procedures and workflows.
• Provide structured, high-quality customer updates throughout investigations.

Requirements:

• 3–5 years in technical support or support engineering for a SaaS or developer-focused product.
• Strong debugging skills across cloud environments, APIs, browser issues, and containerized systems.
• Proficiency in Python or Javascript programming language for scripting and automation tasks.
• Strong understanding of cloud computing platforms, particularly AWS (Amazon Web Services), including services such as EC2, S3, Lambda, and IAM.
• Familiarity with machine learning concepts and frameworks, with experience working with ML models preferred.
• Excellent problem-solving skills and ability to analyze complex technical issues.
• Exceptional communication skills, both written and verbal, with a customer-centric approach.
• Ability to thrive in a fast-paced environment, managing multiple priorities effectively and meeting tight deadlines.
• Strong teamwork and collaboration skills, with the ability to work cross-functionally with engineering, product management, and customer success teams.

Preferred Qualifications:

• Experience with Kubernetes, container orchestration, or infrastructure debugging.
• Knowledge of ML/AI workflows or data labeling pipelines.
• Familiarity with Agile development methodologies.
• Familiarity with REST APIs and integrating with external services.

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 for the United States is $75,000 to $90,000 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.