• You develop and operate AI-native features end-to-end — with a focus on quality, safety, latency, and cost efficiency. • You build agentic applications and chatbots, including tool use, stateful workflows, and (multi-)agent orchestration. • You establish standards and best practices for how we develop, evaluate, and deploy AI systems. • You work closely with product and engineering leadership to translate business requirements into technical solutions. • You actively shape experimentation workflows and long-term AI roadmap decisions.

• Create solutions for customers: Architect and implement intelligent LLM-powered ETL pipelines from scratch to transform unstructured client data into concrete, structured solutions. • Build individual agents : Develop autonomous, multi-agent systems using state-of-the-art frameworks to automate complex, multi-step human business processes. • Keep the quality high : Design and maintain robust evaluation frameworks to systematically quantify AI performance and serve as the gatekeeper of quality for production systems. • Own the whole cycle : Build and deploy high-performance RAG systems and wrap the AI logic into performant, production-ready microservices (e.g., FastAPI). • Grow with your team : Actively engage in creative brainstorming sessions and technical design discussions with teammates to challenge assumptions, jointly solve problems, and grow the team's collective knowledge.

• Develop and maintain our full‑stack applications within a modern JavaScript/TypeScript ecosystem • Design and implement scalable backend services with Node.js and Nest.js • Implement high‑performance frontend solutions using React.js and Next.js • Design and optimize relational databases (PostgreSQL, MySQL, MS SQL) • Containerize and orchestrate services using Docker and Kubernetes • Develop and integrate REST APIs • Build and operate data pipelines, feature stores and ML workflows for our agentic systems • Develop, train and evaluate machine learning models for marketing use cases (attribution, targeting, forecasting, anomaly detection) • Implement retrieval‑augmented generation (RAG) pipelines (embeddings, vector stores, re‑ranking) • Set up and maintain LLM evaluation frameworks, prompt optimization and systematic quality benchmarks • Apply code harnesses (notably Claude Code and Codex) in the daily development process — from planning through implementation to code review • Collaborate closely with the founding team on the product architecture of agentic systems

Role Description At the intersection of Artificial Intelligence, industrial systems, and applied ML research, we are looking for a Senior Machine Learning Researcher to help design and validate novel ML approaches for understanding complex engineering systems from heterogeneous technical data. The project is an early-stage R&D initiative focused on transforming industrial system descriptions, technical documentation, diagrams, specifications, and system data into structured representations that can support downstream modeling, simulation, and automation workflows. The focus of the role has shifted toward a pure ML research profile rather than a primarily engineering / implementation profile. We are looking for someone who can: - Explore research directions - Design experiments - Benchmark model approaches - Validate what is technically feasible in an ambiguous R&D environment Qualifications - Strong background in machine learning research, applied ML, computer vision, multimodal learning, graph ML, representation learning, or related areas - Experience designing and running ML experiments, benchmarking models, and interpreting results - Hands-on experience with deep learning frameworks such as PyTorch or TensorFlow - Ability to work with ambiguous research questions and translate them into testable hypotheses - Experience reading research papers and adapting methods to practical problems - Strong Python skills and ability to build research prototypes - Comfortable working independently in an R&D / PoC environment with evolving requirements - Strong commercial experience with Python and modern AI/ML ecosystems Requirements - Experience with document understanding, diagram understanding, technical drawings, engineering schematics, or similar visual / structured data - Experience with knowledge graphs, graph neural networks, graph embeddings, or structured relationship modeling - Experience with vision transformers, VLMs, self-supervised learning, contrastive learning, or synthetic data generation - Experience with industrial, scientific, simulation, robotics, manufacturing, or physical-system data - Experience with multimodal data fusion, combining visual, textual, structured, or sensor-based data - Familiarity with GCP / Vertex AI or other cloud-based ML environments Benefits At Avenga, everyone matters. We provide equal opportunities in recruitment, career development, and leadership, regardless of race, ethnicity, gender identity, sexual orientation, disability, age, religion, or any other characteristic. We are committed to fostering a work environment where our diverse community of employees, candidates, and business partners actively shapes our growth. By bringing together people from different backgrounds and experiences, we build a workplace where everyone feels free to be themselves while honoring the boundaries of others.