Our goal is to build a small, but excellent team, therefore we are looking for a Senior AI/ML Engineer. If you are excited to work in a very early-stage environment alongside the founding team, stellar data scientists and engineers, using AI at the product core to solve meaningful problems - please reach out.

The Role

As our Senior AI/ML Engineer, you will own the AI brain of Resourcly—building intelligent data understanding, normalization, and semantic matching systems that turn heterogeneous industrial data into structured, comparable knowledge. This is a first-time hire for this role, meaning you'll have significant influence in shaping our AI/ML strategy and architecture from the ground up.

You will transform messy, real-world data—PDFs, images, JSON, XML, CSV, legacy formats—into structured, normalized representations that can be compared, matched, and reasoned about. You'll work closely with our Founding Engineer Soheil, developers, domain experts, and customers to build systems that extract meaning, normalize values, build relationships between entities, and enable intelligent functionality on top of this unified knowledge layer.

🪄 Key Responsibilities

* Own and improve data extraction and normalization pipelines that process heterogeneous data sources (PDFs, images, JSON, XML, CSV) into structured, unified representations using multimodal LLMs

* Build and optimize RAG-based semantic matching systems that map extracted data to canonical catalogs, enabling intelligent comparison across different data sources and formats

* Design and maintain production ML systems with high reliability, including cost optimization, latency improvements, monitoring, and scaling for increasing data volumes

* Drive R&D and experimentation by evaluating new models and techniques, prototyping advanced capabilities, and building rigorous evaluation frameworks and benchmarks

* Contribute to backend engineering (~40%) by building Python services, APIs, and async worker systems that power AI features in production

🚀 Who You Are

* Take full ownership: From research and experimentation through deployment and monitoring in production

* Bring startup mindset with high agency: Comfortable with ambiguity, willing to experiment rapidly and iterate based on results

* Embody "fail fast" mentality: Quick prototyping to validate approaches, but know when to transition to production-quality implementation

* Enjoy customer interaction: Willing to review processing results with industrial engineers and procurement teams to understand quality issues and edge cases

* Low ego, company-first mindset: Prioritize impact over using the "newest or coolest" technology

* Proactive communication: Transparently share progress, blockers, and trade-offs with team and leadership

* Balance pragmatism and innovation: Know when simple heuristics beat complex models, and when to push for state-of-the-art solutions

* Scientific rigor: Rigorous about evaluation, measurement, and validation of ML improvements

💻 Your Capabilities

* You have deep LLM and RAG experience: Extensive hands-on experience with LLM APIs, RAG architectures, prompt engineering, and production LLM systems—you know how to build systems that actually work in production, not just notebooks

* You can process heterogeneous data: Strong experience transforming unstructured (PDFs, images), semi-structured (XML, JSON), and structured (CSV, databases) data into unified, normalized schemas

* You can build semantic matching systems: Comfortable with embeddings, vector databases, similarity search, and building systems that understand when different data represents the same thing

* You can ship reliable production ML: Proven track record deploying ML systems to production, monitoring performance, optimizing costs, and handling failures gracefully

* You have strong Python and backend skills: Ability to build production services, APIs, and async workers—not just ML code, but the infrastructure that runs it

* You have 7+ years of experience in ML engineering, data science, or related roles with demonstrated production ML systems

* You are fluent in English (German fluency as a strong bonus)

* Optional, but a plus: Experience in manufacturing, supply chain, or industrial sectors; computer vision/OCR experience; knowledge graphs and ontology design

📍 Location & Logistics

* 3 days in office (Preferably in Mannheim, Germany - Berlin as a potential option)

* Start: As soon as possible

* Compensation: Top of market (We're constrained by bandwidth, not demand - great time to negotiate)