Deep Learning Engineer-Model Compression (Fixed-term contract)

We are looking to fill this role immediately and are reviewing applications daily. Expect a fast transparent process with quick feedback.

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 5080%.
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 inn 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 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 as well as working 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:

• Masters 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.

Youll 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.