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# Machine Learning Engineer

- **Company:** Multiverse Computing
- **Location:** Barcelona, Spain
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Amazon Web Services, Big Data, Computer Clusters, Code Review, Nvidia CUDA, Software Debugging, Linux, Distributed Computing Environment, Python (Programming Language), Machine Learning, Open Source Technology, Model Compression, Pytorch, DeepSpeed, Large Language Models, Git, Discretization, Isaac Sim, Integration Tests, Kubernetes, Information Technology, ONNX (Open Neural Network Exchange) Format, HuggingFace, Transformer Architectures, SGLang, Reasoning Engines, Slurm, Machine Learning Operations, TensorRT, VLLM, Megatron, Data Pipelines, Docker, Data Generation
- **Published:** November 7, 2026
- **Apply:** https://www.adzuna.es/details/5956241729

## About the Role

* Master's in Computer Science, AI, Mathematics, Physics, Engineering or a related field, or equivalent industry experience.
 * 2+ years of industry experience building ML systems, including at least 1 year hands-on with transformer models or LLMs.
 * Strong Python software engineering: clean, maintainable, tested code; git, code review, unit and integration tests, CI.
 * Strong PyTorch and hands-on experience training or fine-tuning transformer models.
 * Experience running multi-GPU jobs and a working understanding of distributed training (data, tensor and pipeline parallelism; FSDP or DeepSpeed).
 * Comfortable with Linux, containers (Docker or similar) and shared GPU clusters (Slurm or Kubernetes).
 * Experience building data pipelines that process large datasets.
 * Solid ML fundamentals: you understand what happens inside training, not just how to launch it.
 * Good 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 are strong in some and new to others, we still want to hear from you.
 * Experience with the NVIDIA stack: Megatron-LM, Megatron Bridge, NeMo RL, NeMo Gym, NeMo Curator.
 * Hands-on post-training: SFT, DPO, or RL (GRPO, PPO), or frameworks such as verl, TRL or OpenRLHF.
 * Inference engines (vLLM, SGLang, TensorRT-LLM) for serving, synthetic data generation or RL rollouts.
 * Deployment on edge or alternative hardware: llama.cpp, ExecuTorch, ONNX Runtime, OpenVINO, vendor NPU SDKs.
 * Model compression: quantization (GPTQ, AWQ, FP8), pruning, distillation.
 * GPU performance work: profiling (Nsight, PyTorch profiler), Triton or CUDA kernels.
 * Evaluation tooling: lm-evaluation-harness, lighteval, NeMo Evaluator.
 * Experiment tracking and MLOps (MLflow, Weights & Biases), cloud platforms (ideally AWS).
 * Experience with Physical AI tooling: robotics simulation (Isaac Sim/Lab, MuJoCo) or vision-language-action models.
 * Spanish., * Merged contributions to major open-source ML projects (PyTorch, Megatron-LM, vLLM, Hugging Face, etc.).
 * Knowledge of tensor networks or quantum-inspired methods.
 * Publications or patents.