Role Description We are looking for an engineer with practical AI/LLM experience who can build production features in Ruby on Rails. This role is for someone who has worked with AI tools, LLM APIs, prompts, retrieval/context patterns, or AI-assisted workflows - and who also has the engineering depth to turn those ideas into reliable, tested, maintainable software. - Designing and building AI/LLM-assisted product features - Integrating LLM APIs into Ruby on Rails systems - Combining AI approaches with business logic, tests, fallbacks, and production safeguards - Owning features from technical design to production - Improving reliability, accuracy, consistency, latency, cost, security, and maintainability - Writing and maintaining automated tests, including unit, integration, and E2E tests - Debugging production issues and improving system behavior over time Qualifications - Practical experience building with AI/LLM technologies, not just using AI tools casually - Strong Ruby on Rails experience, ideally at a senior level - Good judgment about when AI is useful and when simpler engineering is better - Strong backend skills, including databases, API design, performance, background jobs, and security - Ability to break down ambiguous problems into clear technical solutions Requirements - CI/CD, background jobs, or AWS experience (nice to have) - Experience with text-processing, localization, translation, or multilingual products (nice to have) Benefits - Stable & self-funded: Founded in 2007, profitable, and independent. - Industry leaders: Creators of WPML, the #1 multilingual plugin for WordPress, used on 1.5M+ sites - Fully remote, async-friendly: Work from anywhere within EMEA hours - Engineering-driven culture: Ownership, autonomy, testing, and thoughtful problem-solving - Fair compensation and great benefits: Designed to support your well-being both inside and outside of work.

• Ship full-stack AI projects end-to-end on a product built in Ruby on Rails backend and React frontend • Design and run experiments to validate AI features • Build and integrate components for AI infrastructure • Improve processes, tools, and systems to scale AI features • Help stay up-to-date with cutting-edge AI research • Work closely with Circle's leaders and designers • Foster a bias for action by prioritizing speed of iteration

• Lead the design, build, deployment, and continuous improvement of WAI's AI-ready data and platform foundation across sales, inventory, planning, catalog, customer, order, product, and related business systems. • Design, build, and govern a centralized data lake that consolidates critical data from ERP and other core business systems into a single trusted foundation, enabling AI tools, models, and analytics to reliably access enterprise data. • Identify repetitive and manual tasks, use process mining to uncover workflow bottlenecks, and implement RPA solutions to improve efficiency and streamline operations. • Own technical architecture for AI/ML/LLM workflows, RAG, embeddings, vector search, structured data query, dashboards, APIs, model serving, and monitoring. • Connect, ingest, clean, validate, normalize, and automate data pipelines from structured and unstructured sources, including enterprise systems, reports, documents, PDFs, spreadsheets, and business notes.

Role Description For decades, Cadmould has been one of the fastest and most advanced injection molding simulators on the market, trusted across the plastics industry. Then we built something the field had never seen: Cadmould AI Solver, the first Large Engineering Model (LEM) for plastic injection molding, a transformer-based neural physics model that delivers high-fidelity results up to 1,000x faster than classical solvers. It turns simulation from a slow validation step into something engineers can explore in real time. This role can be performed from our office in Würselen near Aachen or remotely from Germany, with occasional travel for team events and on-sites. What You Will Do - Work alongside our research engineers and domain experts to advance our AI Solver: the model, training, datasets, and evaluation that capture the complexity of real-world injection molding. - Improve the model by getting to know our transformer-based AI Solver inside out: its architecture, training strategies, data generation, and design trade-offs. Then iterate on architecture, training objectives, and learning strategies to push accuracy and generalization. - Run and analyze experiments by designing experiments, analyzing model behavior, documenting findings, and translating them into concrete improvements to data, training, and model quality. - Build shared understanding by communicating results clearly and contributing to reproducible workflows and shared technical context across the team. - Strengthen the platform by extending our training, evaluation, and deployment workflows and infrastructure. - Pitch in beyond the model as a small, fast-moving team, stepping outside our lane when it counts, from the AI service side to agentic AI features for product experiments. Qualifications - Background in Machine Learning, Physics, (Computational) Engineering, Applied Mathematics, or a related field, with 3+ years of relevant experience, or a strong foundation from personal or research projects. - Solid, practical machine learning fundamentals with PyTorch, TensorFlow, or similar, plus the usual data-wrangling stack (NumPy and friends). - Ability to turn research papers into working code and a desire to understand model behavior. - Structured and methodical in work, documenting experiments, keeping work reproducible, and explaining findings clearly. - Experience with coding agents as a system to optimize, not just a gadget. - Fluency in English; German is a plus. Requirements - Nice to have: Transformers or sequence-based models. - Geometric deep learning, GNNs, or learning on structured 3D data (point clouds, meshes). - Scientific machine learning, surrogate modeling, or physics-based data. - Cloud environments for machine learning workloads (training, experimentation, deployment). - Experience with experiment tracking and versioning tools (MLflow, Weights & Biases, DVC), plus solid software engineering fundamentals (testing, version control, containerization). - You won't check every box. If you know your gaps and how to close them, apply. Benefits - A real technical challenge: reshaping a proven simulation engine for a market moving to cloud and AI. - Ownership and impact: about 40 people, where your decisions shape the product and the business. - Modern tooling: Notion, GitHub, Linear, coding agents. - Direct and honest culture: candid feedback is standard practice, both internally and externally. - No micromanagement.