ppWe’re looking for an AI Model Engineer who thrives at the frontier of applied AI, representation learning, and high-performance model design. /p h3Role Summary /h3 pWe’re seeking an AI Model Engineer to help build the model foundation for AI-native data infrastructure. You’ll work on embedding models, efficient neural architectures, semantic representation, and high-throughput inference systems that make large-scale object storage searchable, understandable, and automation‑ready. /p pThis role is not about building a conventional chatbot or application‑layer RAG product. It is about designing and optimizing the models that allow data platforms to understand files, metadata, documents, logs, images, and structured content at scale. /p pYou’ll work closely with engineering leadership to prototype, benchmark, and productionize models that can operate over massive volumes of enterprise data with strong performance, practical latency, and real‑world retrieval quality. /p h3What You’ll Be Doing /h3 ul liDesign, train, fine‑tune, and evaluate embedding models for documents, objects, metadata, and multimodal data. /li liExplore high-performance neural architectures, including CNN‑inspired models, gated convolutional blocks, efficient sequence models, transformers, and hybrid architectures. /li liBuild models optimized for semantic search, classification, clustering, tagging, similarity matching, and automated data discovery. /li liOptimize inference performance through batching, quantization, distillation, pruning, compilation, and accelerator‑aware deployment. /li liEvaluate model quality using retrieval, recall, clustering, semantic similarity, and downstream search benchmarks. /li liWork with large‑scale datasets extracted from object storage, structured files, logs, documents,



and enterprise data sources. /li liPrototype and benchmark new model architectures rapidly, then help bring the best ideas into production. /li liCollaborate with systems engineers to ensure models are deployable at high throughput and aligned with real infrastructure constraints. /li liHelp shape the AI model layer behind next‑generation intelligent storage, search, and data automation. /li /ul h3What We Need to See /h3 ul liStrong hands‑on experience with PyTorch, TensorFlow, JAX, or similar model development frameworks. /li liPractical background in embeddings, representation learning, neural networks, transformers, CNNs, or sequence models. /li liExperience evaluating models for retrieval, semantic search, clustering, classification, or similarity matching. /li liStrong understanding of model‑performance tradeoffs: quality, latency, throughput, memory footprint, and cost. /li liExperience with model optimization techniques such as quantization, distillation, pruning, ONNX, TensorRT, or similar tooling. /li liAbility to work with large datasets and build repeatable training, evaluation, and benchmarking workflows. /li liStrong Python skills and comfort working with production engineering teams. /li liCuriosity, technical depth, and the ability to move from research prototype to production‑ready model behavior.



/li /ul h3Ways to Stand Out /h3 ul liExperience building or fine‑tuning embedding models for enterprise search, document AI, code search, multimodal search, or large‑scale retrieval. /li liHands‑on work with CNN‑based, gated convolutional, or efficient sequence architectures. /li liExperience with GPU inference, CUDA, Triton, TensorRT, ONNX Runtime, or accelerator‑aware model serving. /li liBackground in contrastive learning, Siamese networks, CLIP‑style training, sentence‑transformers, or domain‑specific embeddings. /li liExperience working with PDF, Office files, logs, source code, Parquet, Avro, image data, or other complex enterprise data formats. /li liPublished research, open‑source contributions, or strong practical work in retrieval models, model compression, or high‑throughput inference. /li liInterest in building models for infrastructure‑scale AI systems rather than only application‑layer products. /li /ul h3Why Join Us /h3 pFastS3 is building AI‑native data infrastructure from the ground up. Our vision is to make enterprise data searchable, programmable, and useful to AI systems directly within the data layer. /p pYou’ll join a team working at the intersection of storage, retrieval, machine learning, and high‑performance systems. If you’re excited by the idea of building fast, practical, production‑grade models that help power the next generation of intelligent infrastructure, this is your chance to do it at scale. /p pWe’re growing our team in Madrid and looking for engineers who want to push the frontier of applied AI, not just consume it. /p pFastS3 is proud to be an inclusive, equal opportunity employer committed to diversity, equity, and accessibility for all. /p /p #J-18808-Ljbffr

📌 AI Model Engineer — Embeddings & High-Performance Neural Architectures (Madrid)
🏢 FastS3
📍 Madrid