❗️Please note that this role is office based for Valencia, Spain (RAS Valencia - Servicios Profesionales de Recursos Humanos).

❗️If you need to be relocated, we take care of the process.

We are developing an innovative platform for intelligent gameplay analysis and complex in-game scenario automation. Our team builds internal tooling that bridges the gap between Computer Vision, text analysis, and modern LLM architectures to achieve deep, real-time understanding of game mechanics.

Key Responsibilities:

• Core Development: Designing and maintaining an in-house AI-powered service for intelligent automation.
• Computer Vision: Training and calibrating Object Detection and Classification models (CNNs, DETR) on unique proprietary datasets.
• Intelligent Analysis: Integrating OCR solutions and LLMs to interpret data and structure information from dynamic content.
• MLOps: Building pipelines for model retraining based on "low-confidence" predictions (Active Learning).
• Infrastructure: Containerizing solutions and ensuring their scalability and performance.

Our Tech Stack & Requirements:

• Experience: 4+ years in software development (Backend / Full Stack / SDET).
• AI/ML Background: Proven hands-on experience with Detection Transformers (DETR) or modern CNN architectures.
• LLM & Agents: Understanding of AI Agent principles; experience building tools and integrations with LLMs (LangChain, Indexing, Tool-use).
• Python: Strong proficiency in Python and experience in AI tool development.
• Engineering Excellence: Deep understanding of TDD, clean architecture principles, and database management (PostgreSQL/MySQL).
• Networking: Experience designing APIs (REST, GraphQL, WebSockets).
• English: Upper-Intermediate (technical documentation, professional communication).

Nice to Have:

• Experience with Kubernetes and Docker.
• Understanding of microservices architecture.
• Experience setting up CI/CD pipelines (GitLab CI, GitHub Actions).

We're building an AI copilot for web testing automation. It enables manual and automation QA engineers to turn their manual testing flows into production-ready automated E2E testing scripts, saving hours of manual work and coding. We are a small team with zero bureaucracy, focusing on real users. We optimize for shipping value - not hype.

About the role:

You'll own the top of the funnel: outbound, qualification, and getting leads to sit down for a conversation. Demos, onboarding, and retention are staying with the founder for now.

Phase one: Get individual Automation QAs to try the product, aiming for fast adoption and no procurement. Once we hit 20+ weekly users, we’ll move on to phase two, where we’ll shift the focus to B2B – qualified meetings with QA Leads and Engineering Managers.

Your focus in the first three months:

• Build targeted lists: Automation QAs at companies with web products and Playwright, Cypress, or Selenium in their stack.
• Run outbound sequences (LinkedIn is the baseline, but you decide the channels). A/B test weekly.
• Design qualification gates (forms and scoring) to filter leads before the founder calls.
• Reach 20+ weekly active users after month one – ICP-qualified and giving direct feedback to engineering.
• Document what works into a repeatable playbook.

Growth path:

This role expands as your strategies deliver results. Once the user pipeline works, you can take over demos and run B2B sales end to end. From there, the natural next step is Growth Lead (funnel optimization and scaling the playbook) or Account Executive (owning deal execution).

Why to join:

• You'll be building a strategy that scales the product, not filling a quota. What you create from scratch becomes the foundation.
• Your outreach turns into users who talk directly to engineering, and their feedback ships as features within days.
• AI for developer tools is a market with real pull, and QA automation is one of its sharpest pain points.
• We’re backed by JetBrains – financially secure with no reliance on external VC funding.

Who we're looking for:

You should have a high level of agency and be quant-minded – you live in funnel metrics, run experiments, and are comfortable saying "no" to leads that are a bad fit. 

The ideal candidate will:

• Have done 0-to-1 outbound for an early-stage devtool, QA tool, infrastructure product, or similar.
• Have proven cold outbound results, instead of being dependent on warm inbound.
• Be able to talk credibly about flaky tests, CI pain, and Playwright vs. Cypress. You don't need to be an engineer, but you do need domain empathy.
• Possess strong written communication skills, with the ability to craft short, sharp messages that get replies.

Nice to have:

• A QA or SDET background – you've felt the pain yourself before moving to sales or BD.
• Exposure to security/compliance objections – enough to pre-empt and route.
• Content skills – your LinkedIn posts get engagement, not just your DMs.
• Experience as one of the first 10 employees at a startup.

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