LLM engineer for foundational AI models

Multiverse Computing is a deep-tech company that develops quantum software and AI solutions. Its products include CompactifAI, a compression tool for foundational AI models, and Singularity, a quantum and quantum-inspired optimization platform for complex industry problems.

• Train and post-train LLMs and multimodal models on multi-node GPU clusters using SFT, preference optimisation, RL, and distillation
• Design model compression techniques and healing recipes to recover the quality of compressed models
• Build RL environments, reward functions, and verifiers for reasoning, coding, and agentic tasks; extend RL methods to world models and Physical AI
• Research and prototype post-transformer and hybrid architectures, from small-scale ablations to scaling recipes
• Build data pipelines for curation, filtering, deduplication, and large-scale synthetic data generation
• Design ablations and evaluations using current benchmark suites and internal evaluations
• Port and optimise models for inference on accelerators and edge chips; profile latency, memory, and throughput
• Document experiments and results in internal reports and, where relevant, papers and open-source releases
• Review code, mentor teammates, and follow and bring in the latest research

• Master’s or PhD in Computer Science, AI, Mathematics, Physics, or a related field, or equivalent industry experience
• At least 4 years of hands-on experience training or post-training transformer models
• Strong PyTorch skills and ability to work below the library level with model code, custom losses, and parallelism configuration
• Experience with distributed training frameworks and parallelism on multi-node clusters with Slurm/K8s
• Hands-on post-training experience with SFT, DPO, and at least one RL method
• Strong foundations in linear algebra, optimisation, and probability, and understanding of training dynamics
• Good understanding of GPU fundamentals, including memory hierarchy, mixed precision, profiling, and throughput
• Rigorous experimental practice, including ablation design, reproducibility, experiment tracking, and noise-aware evaluation
• Strong debugging and communication skills
• Fluent English
• Будет плюсом: NVIDIA stack or similar frameworks, RL infrastructure, model compression, post-transformer architectures, Physical AI, multimodal model training, inference on alternative hardware, large-scale data processing and synthetic data generation, evaluation tooling, familiarity with Artificial Analysis, publications at top venues, notable open-source contributions, models released on Hugging Face, Spanish

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