Deep Learning Engineer-Model Compression

Why join us? We are a European deep-tech leader in quantum and AI, backed by major global strategic investors and strong EU support. Our groundbreaking technology is already transforming how AI is deployed worldwide — compressing large language models by up to 95% without losing accuracy and cutting inference costs by 50–80%.Joining us means working on cutting‑edge solutions that make AI faster, greener, and more accessible — and being part of a company often described as a “quantum‑AI unicorn in the making.” We offer Competitive annual salary Two unique bonuses: signing bonus at incorporation and retention bonus at contract completion. Relocation package (if applicable). Fixed‑term contract ending in June 2026. Hybrid role and flexible working hours Be part of a fast‑scaling Series B company at the forefront of deep tech. Equal pay guaranteed. International exposure in a multicultural, cutting‑edge environment. Job Overview We are seeking a skilled and experienced Deep Learning Engineer (Senior and Mid-level) with a strong background in deep learning to join our team. In this role you will have the opportunity to leverage cutting‑edge quantum and AI technologies to lead the design, implementation, and improvement of our computer vision and language models, and to work closely with cross‑functional teams to integrate these models into our products. You will have the opportunity to work on challenging projects, contribute to cutting‑edge research, and shape the future of LLM and AI technologies. As a Deep Learning Engineer for Model Compression, you will Design, train, and optimize deep learning models from scratch (including LLMs and computer vision models), working end‑to‑end across data preparation, architecture design, training loops, distributed compute, and evaluation. Apply and further develop state‑of‑the‑art model compression techniques, including pruning (structured/unstructured), distillation, low‑rank decomposition, quantization (PTQ/QAT), and architecture‑level slimming. Build reproducible pipelines for large‑model compression, integrating training, re‑training, search/ablation loops, and evaluation into automated workflows. Design and implement strategies for creating, sourcing, and augmenting datasets tailored for LLM pre‑training and post‑training, and computer vision models. Fine‑tune and adapt language models using methods such as SFT, prompt engineering, and reinforcement or preference optimization, tailoring them to domain‑specific tasks and real‑world constraints. Conduct rigorous empirical studies to understand trade‑offs between accuracy, latency, memory footprint, throughput, cost, and hardware constraints across GPU, CPU, and edge devices. Benchmark compressed models end‑to‑end, including task performance, robustness, generalization, and degradation analysis across real‑world workloads and business use cases. Perform deep error analysis and structured ablations to identify failure modes introduced by compression, guiding improvements in architecture, training strategy, or data curation. Design experiments that combine compression, retrieval, and downstream finetuning, exploring the interaction between model size, retrieval strategies, and task‑level performance in RAG and Agentic AI systems. Optimize models for cloud and edge deployment, adapting compression strategies to hardware constraints, performance targets, and cost budgets. Integrate compressed models seamlessly into production pipelines and customer‑facing systems. Maintain high engineering standards, ensuring clear documentation, versioned experiments, reproducible results, and clean modular codebases for training and compression workflows. Participate in code reviews, offering thoughtful, constructive feedback to maintain code quality, readability, and consistency. Required Minimum qualifications Master’s or Ph.D. in Computer Science, Machine Learning, Electrical Engineering, Physics, or a related technical field. 3+ years of hands‑on experience training deep learning models from scratch, including designing architectures, building data pipelines, implementing training loops, and running large‑scale distributed training jobs. Proven experience in at least one major deep learning domain where training from scratch is standard practice, such as computer vision (CNNs, ViTs), speech recognition, recommender systems (DNNs, GNNs), or large language models (LLMs). Strong expertise with model compression techniques, including pruning (structured/unstructured), distillation, low‑rank factorization, and architecture‑level optimization. Demonstrated ability to analyze and improve model performance through ablation studies, error analysis, and architecture or data‑driven iterative improvements. In‑depth knowledge of foundational model architectures (computer vision and LLMs) and their lifecycle: training, fine‑tuning, alignment, and evaluation. Solid understanding of training dynamics, optimization algorithms, initialization schemes, normalization layers, and regularization methods. Hands‑on experience with Python, PyTorch and modern ML stacks (HuggingFace Transformers, Lightning, DeepSpeed, Accelerate, NeMo, or equivalent). Experience building robust, modular, scalable ML training pipelines, including experiment tracking, reproducibility, and version control best practices. Practical experience optimizing models for real‑world deployment, including latency, memory footprint, throughput, hardware constraints, and inference‑cost considerations. Excellent problem‑solving, debugging, performance analysis, test design, and documentation skills. Excellent communication skills in English, with the ability to document and explain design decisions, experiment results, and trade‑offs to both technical and non‑technical stakeholders. Preferred Qualifications Ph.D. with research focus on efficient deep learning, model compression, sparse methods, quantization, distillation, or neural architecture search. Demonstrated track record of open‑source contributions to deep learning frameworks, compression libraries, or model efficiency tooling (e.g., PyTorch, HuggingFace, TensorRT, ONNX Runtime, Sparsity/Pruning libraries). Strong background in distributed training, GPU acceleration, mixed‑precision training, and optimization for multi‑node or multi‑GPU settings (AWS, Azure, HPCs). Hands‑on experience with hardware‑aware model design, including optimizing models for GPUs, CPUs, mobile/edge accelerators, or specialized inference chips. Experience implementing or extending neural architecture search (NAS or structural pruning methods to discover efficient sub‑architectures. Publication record in top ML or systems conferences (e.g., NeurIPS, ICML, ICLR, MLSys) related to compression, efficient ML, or large‑scale training. About Multiverse Computing Founded in 2019, we are a well‑funded, fast‑growing deep‑tech company with a team of 180+ employees worldwide. Recognized by CB Insights (2023 & 2025) as one of the Top 100 most promising AI companies globally, we are also the largest quantum software company in the EU.Our flagship products address critical industry needs: CompactifAI → a groundbreaking compression tool for foundational AI models, reducing their size by up to 95% while maintaining accuracy, enabling portability across devices from cloud to mobile and beyond. Singularity → a quantum and quantum‑inspired optimization platform used by blue‑chip companies in finance, energy, and manufacturing to solve complex challenges with immediate performance gains. You’ll be working alongside world‑leading experts in quantum computing and AI, developing solutions that deliver real‑world impact for global clients. We are committed to an inclusive, ethics‑driven culture that values sustainability, diversity, and collaboration — a place where passionate people can grow and thrive. Come and join us As an equal opportunity employer, Multiverse Computing is committed to building an inclusive workplace. The company welcomes people from all different backgrounds, including age, citizenship, ethnic and racial origins, gender identities, individuals with disabilities, marital status, religions and ideologies, and sexual orientations to apply. #J-18808-Ljbffr