Machine Learning Engineer (Mid-Level)

📋 Job Overview

Join Multiverse Computing, a fast-growing deep-tech company recognized by CB Insights as one of the 100 most promising AI companies globally. As a Machine Learning Engineer, you will work alongside world-leading experts to develop cutting-edge AI solutions for global clients across finance, energy, manufacturing, telecom, and industrial sectors.

🏢 About Multiverse Computing

Multiverse Computing is the largest quantum software company in the EU, with 300+ employees worldwide. Founded in 2019, we are committed to fostering an inclusive, ethics-driven culture grounded in sustainability, diversity, and collaboration. Our mission is to enable organizations to gain a meaningful competitive edge through cutting-edge AI and quantum technologies.

🎯 The Role

As a Machine Learning Engineer, you will build and maintain the training stack our researchers use every day, develop scalable data pipelines, create evaluation pipelines, and optimize training and inference processes. You will also package, optimize, and benchmark models for deployment, and ensure reproducibility through containers, configs, experiment tracking, CI, code review, and documentation.

✅ Key Responsibilities

• Build and maintain the training stack for researchers: 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 for reproducible and comparable benchmark results.
• Build RL environments, verifiers and reward pipelines for reasoning, coding and agentic tasks.
• Profile and optimize training and inference: GPU utilisation, memory, parallelism configuration, checkpointing, I/O.
• Package, optimize and benchmark models for deployment, including on non-NVIDIA and edge hardware.
• Turn research ideas into clean, tested, reusable code and run experiments with the research team.
• Ensure reproducibility through containers, configs, experiment tracking, CI, code review and documentation.

📌 Required Qualifications

• 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: understanding what happens inside training, not just how to launch it.
• Good debugging and communication skills. Fluent English.

⭐ Desirable Experience

• 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.

🎁 Benefits

• Indefinite contract.
• Equal pay guaranteed.
• Variable performance bonus.
• Signing bonus.
• 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.

🛂 Visa & Eligibility

We offer work visa sponsorship (if applicable).