Senior LLM Engineer, Multiverse Computing

• Master’s or PhD in Computer Science, AI, Mathematics, Physics or a related field, or equivalent industry experience
• Senior: 4+ years of hands-on experience training or post-training transformer models
• Strong PyTorch. You are comfortable working below library level: model code, custom losses, parallelism configuration
• Experience with distributed training (FSDP, DeepSpeed or Megatron-LM; data, tensor, pipeline and expert parallelism) on multi-node clusters with Slurm/K8s
• Hands-on post-training experience: SFT, DPO, and at least one RL method (PPO, GRPO or similar)
• Solid mathematical foundations in linear algebra, optimisation and probability, and an understanding of training dynamics
• Good understanding of GPU fundamentals: memory hierarchy, mixed precision, profiling, throughput
• Rigorous experimental practice: ablation design, reproducibility, experiment tracking, noise-aware evaluation
• Strong debugging and communication skills. Fluent English
• We don’t expect anyone to have all of these. Our research covers many areas and nobody on the team is an expert in all of them. If you have deep experience in some and none in others, we still want to hear from you

Desirable

• We are looking for a Senior LLM Engineer to join our Research team. You will work across the full model lifecycle: data, distributed training, post-training, compression, evaluation and efficient inference
• Our past work includes compressing and healing open-weight models, post-training with SFT, RL and distillation, multimodal training, knowledge editing, and building our own family of models
• Priorities change quickly in research, so we value strong fundamentals over narrow specialisation. When it makes sense, we publish papers and release models and code
• Train and post-train LLMs and multimodal models on multi-node GPU clusters using SFT, preference optimisation, RL (e.g. GRPO), and on-policy and offline distillation
• Design compression techniques (quantization, pruning, quantization-aware training, quantum-inspired tensor-network methods) and healing recipes that recover the quality of compressed models
• Build RL environments, reward functions and verifiers for reasoning, coding and agentic tasks, and extend RL methods to world models and Physical AI
• Research and prototype post-transformer and hybrid architectures (state-space models, linear attention, MoE), from small-scale ablations to scaling recipes
• Build data pipelines for curation, filtering, deduplication and large-scale synthetic data generation
• Design rigorous ablations and evaluations on current benchmark suites (reasoning, coding, long-context, instruction following, agentic) as well as internal evals
• Port and optimise models for inference on various accelerators and edge chips, profiling latency, memory and throughput
• Write up experiments and results as internal reports and, where relevant, papers and open-source releases
• Review code, mentor teammates, and follow and bring in the latest research