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.
We’re looking for an ambitious AI Engineer to transform how our go-to-market team operates. San Francisco, Austin, or Lisbon are preferred locations where you can collaborate with team members, but the position is remote or hybrid.
As the founding GTM engineer, you will architect and build the AI-powered systems that power our entire GTM motion, owning the technology stack, partnering with stakeholders to define the workflows, deploying AI agents and apps, and continuously improving how the company acquires, activates, and grows customers.
This is a highly hands-on role for someone who enjoys shipping systems, experimenting rapidly, and solving messy real-world problems with software.
Why This Role MattersThis role is a force multiplier for the entire company. You’ll build the infrastructure that allows the GTM team to move faster, learn faster, and scale without proportional headcount. You’ll help us:
- Turn GTM from a set of tools into a cohesive, intelligent system
- Scale workflows without scaling headcount
- Improve signal quality and timing for marketing and sales
- Increase learning velocity across the entire funnel
- Shape how an open-source, bottom-up product grows into enterprise adoption
- Design, build, and maintain AI systems across marketing, PLG, sales, and post-signup activation
- Own integrations and workflows across tools like CRM, marketing automation, product analytics, data warehouse, enrichment tools, and internal services
- Build internal tools and lightweight services that accelerate GTM teams
- Replace manual workflows with scalable, automated systems
Design and deploy AI-driven workflows and agents that augment GTM teams.
Examples include:
- Lead qualification, inbound lead routing, and enrichment
- Personalized lifecycle marketing and outbound messaging
- Sales assist tools for deal intelligence and account research
- Automated account insights, pipeline analysis, and deal lifecycle management
- User onboarding, activation, and expansion signals
You’ll continuously improve these systems using real performance data and feedback loops.
Growth: PLG + Sales-Assisted Motion- Support a hybrid GTM model where open-source and self-serve users graduate into paid, sales-assisted customers
- Partner with Data & Growth to build systems that surface product signals (usage, intent, readiness) to marketing and sales teams at the right time
- Partner closely with Product & Marketing to turn usage insights into GTM leverage
Build the infrastructure that allows the GTM team to learn quickly and iterate with confidence.
- Design and run experiments across acquisition, activation, conversion, and expansion
- Build experimentation frameworks (feature flags, A/B testing, measurement systems)
- Instrument systems to capture reliable data and insights
- Rapidly iterate based on results, not assumptions
Create tight feedback loops between users, product signals, and GTM actions.
- Partner with head of , accessible, and actionable across teams
- Turn qualitative and quantitative insights into system improvements
- Continuously optimize workflows, models, and automation based on real outcomes
This is fundamentally an engineering role, not traditional GTM operations. We’re looking for someone who enjoys building systems, not managing tools.
- Strong engineering background (software, data, or growth engineering)
- Comfortable shipping production code (APIs, services, scripts, internal tools)
- Experience working with data pipelines, analytics, and experimentation frameworks
- Hands-on experience with AI/LLMs, prompt engineering, or agent-based systems
- Ability to work cross-functionally with Product, Engineering, Marketing and Sales
- Comfortable operating with ambiguity and defining the problem as you go
- Strong communicator who can translate between technical and GTM stakeholders
- Experience with open-source or developer-focused products
- Familiarity with CRM systems (e.g., Salesforce, HubSpot), CDPs, and marketing automation
- Experience building internal tools for sales or marketing teams
- Prior work in B2B SaaS, data, or AI-first companies
Top Skills
What We Do
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.
