Machine Learning Engineer

• 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.

• Indefinite contract.
• Equal pay guaranteed.
• Variable performance bonus.
• Signing bonus.
• We offer work visa sponsorship (if applicable).
• Relocation package (if applicable).
• Private health insurance.
• Eligibility for educational budget according to internal policy.
• Hybrid opportunity.
• Flexible working hours.
• Language classes and discounted lunch options.
• A high-performance, collaborative environment, operating at pace on cutting-edge technologies.
• Career plan. Opportunity to learn and teach.

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. Inscribirse en esta oferta Recibir ofertas similares por correo electrónico No gracias, llévame a la oferta de empleo Al crear una alerta, aceptas nuestros Términos y condiciones y Política de privacidad, y el uso de cookies. Inscribirse en esta oferta

Multiverse Computing is a fast-growing deep-tech company founded in 2019 and recognized by CB Insights as one of the 100 most promising AI companies globally. We are the largest quantum software company in the EU, with 300+ employees worldwide and growing, delivering advanced solutions trusted by leading global enterprises across several critical industries, including finance, energy, manufacturing, telecom, and industrial sectors.

Our mission is to enable organizations to gain a meaningful competitive edge through cutting-edge AI and quantum technologies.

You’ll work alongside world-leading experts, developing solutions that deliver real-world value for global clients. We are committed to fostering an inclusive, ethics-driven culture grounded in sustainability, diversity, and collaboration - a place where ambitious, curious people take ownership, push boundaries, and grow. Come and join us.

As a Machine Learning Engineer, you will:

• Build and maintain the training stack our researchers use every day: multi-node training and post-training pipelines (SFT, distillation, RL) built on Megatron-LM, Megatron Bridge and NeMo RL.
• Build scalable data pipelines for curation, filtering, deduplication, tokenization and synthetic data generation.
• Build and maintain evaluation pipelines, so benchmark results are reproducible and comparable across models and checkpoints.
• Build RL environments, verifiers and reward pipelines for reasoning, coding and agentic tasks, and keep rollouts fast and stable.
• Profile and optimise training and inference: GPU utilisation, memory, parallelism configuration, checkpointing, I/O.
• Package, optimise and benchmark models for deployment, including on non-NVIDIA and edge hardware.
• Turn research ideas (new architectures, compression methods, training recipes) into clean, tested, reusable code, and run experiments together with the research team.
• Keep our work reproducible through containers, configs, experiment tracking, CI, code review and documentation.