AI Engineer (GTM)

HumanSignal

3d•Hybrid

About The Position

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 Role 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 Matters This 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

Requirements

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

Nice To Haves

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

Responsibilities

Build & Automate the foundational GTM AI stack
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.
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
Continuously improve these systems using real performance data and feedback loops.
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