🤝 About us

FounderNest is an AI-native company helping corporations understand markets, map ecosystems, and generate strategic insights from complex data.

We work with +50M companies and +1.2B data points, combining real-time analytics, search, and Generative AI to deliver high-quality, explainable insights.

AI is not a feature at FounderNest, it is core infrastructure. We build production-grade LLM systems, retrieval pipelines, internal agents, and evaluation frameworks designed to operate reliably at scale.

🎯 Why this role exists

We are entering a phase where AI is no longer just part of the product, it is the product.

As we scale conversational interfaces and agent-based workflows, the complexity of reasoning over data, connecting systems, and ensuring quality has increased significantly. Many of our hardest problems sit at the intersection of AI, search, and retrieval systems. This is not a pure search engineering role, but hands-on experience building or evolving search/retrieval capabilities is important for success.

We are looking for someone who can help us build and evolve these systems end-to-end, raising the bar of how we operate AI in production.

This role sits at the intersection of product engineering, AI systems, and data.

Ultimately, your work will shape how users interact with complex data through AI, turning raw information into actionable insights.

🧠 What you will do

You will work end-to-end on AI-powered product capabilities, combining product thinking with system-level engineering.

• Design and implement AI-powered search and retrieval experiences that combine LLMs, tools, structured data, ranking/relevance signals, and context retrieval

• Connect AI systems to real product surfaces (search, company data, workflows) and ensure they deliver meaningful outcomes

• Own AI quality by using and building evaluation frameworks, diagnosing failure modes, and improving reliability

• Operate across the stack, integrating AI with backend services and data pipelines, balancing cost, latency, and quality

• Collaborate with product, data, and design to translate ambiguous problems into working solutions and iterate based on feedback

• Break down ambiguous problems into simple solutions, iterating quickly to deliver value before scaling complexity

🧩 What we are looking for

We are not hiring a specific title, we are hiring someone who can operate across systems.

You might come from product engineering, ML/AI engineering, Data Science or a generalist background, but this role is best suited for someone who has shipped product-facing systems where AI and search/retrieval were central to the user experience.

From an AI perspective, you understand how to design systems with LLMs, not just call them. You are comfortable with concepts like RAG, context structuring, retrieval quality, model trade-offs, and the importance of evaluation in production systems.

From an engineering perspective, you can build and ship reliable systems. You are comfortable working across APIs, services, and data pipelines, and you care about observability, performance, and maintainability.

From a product perspective, you take ownership of problems, not just tasks. You are proactive, comfortable with ambiguity, and focused on delivering real value to users.

From an AI-native workflow perspective, you use AI as a core part of your engineering workflow. You understand its tradeoffs and limitations, and you think in terms of systems, not just prompts. You leverage AI to accelerate development while maintaining quality and control.

From a search/retrieval perspective, you have hands-on experience helping users find, rank, retrieve, or reason over complex information. You understand that quality depends not only on the model, but also on indexing, retrieval strategy, relevance, context selection, and feedback loops.

👉 Important: hands-on experience working with search, retrieval, or relevance systems. This does not need to mean being a pure search engineer, but you should have built or meaningfully contributed to systems involving ranking, indexing, query understanding, semantic search, relevance optimization, or large-scale data retrieval.

Given the nature of our product, this is a high-leverage area and a key part of the role.

We value curiosity, pragmatism, and low ego. You should be someone who learns fast, collaborates well, and helps raise the level of the team.

🛠️ Tech stack

Our stack includes Typescript and Python, PostgreSQL and MongoDB, BigQuery and DBT, and infrastructure running on Kubernetes (AWS). On the AI side, we work with multiple LLM providers, RAG pipelines, internal agents, and evaluation systems.

You don’t need experience with all of this, but you should be comfortable navigating similar environments.

🚀 What makes this role different

This role focuses on building the core systems behind our AI-powered product experience, especially around search, retrieval, and reasoning over complex data.

You will help shape how AI and retrieval systems are designed, evaluated, and scaled at FounderNest, working across product, data, and engineering.

Your impact will not only be what you ship, but how the system evolves.

🧭 Final note

We are not looking for someone who knows everything, but for someone who can connect the dots, learn fast, and push the system forward.