{"id":3901251,"url":"https://alion.io/job/multiverse-computing-machine-learning-engineer-mid-level","title":"Machine Learning Engineer (Mid-Level)","company":{"id":1535,"name":"Multiverse Computing","domain":"multiversecomputing.com","url":"https://alion.io/company/multiverse-computing","size_band":"201-500","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Teamtailor","truth_index":{"grade":"A","score":92,"open_postings":3,"ghost_share":0,"stale_share":0.333,"repost_share":0,"time_to_fill_p50_days":58,"computed_at":"2026-11-06T05:58:05Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"middle","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Barcelona, Spain"],"countries":["ES"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":null,"experience_years_min":2,"visa_sponsorship":true,"relocation_package":true,"has_equity":false,"technologies":[{"name":"DeepSpeed","optional":false},{"name":"Docker","optional":false},{"name":"Fine-tuning","optional":false},{"name":"FSDP","optional":false},{"name":"Knowledge Distillation","optional":false},{"name":"Kubernetes","optional":false},{"name":"Linux","optional":false},{"name":"Machine Learning","optional":false},{"name":"Megatron-LM","optional":false},{"name":"Model Distillation","optional":false},{"name":"NVIDIA NeMo","optional":false},{"name":"Post-training","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"SFT","optional":false},{"name":"SLURM","optional":false},{"name":"Synthetic Data","optional":false},{"name":"Tokenization","optional":false},{"name":"AWQ","optional":true},{"name":"AWS","optional":true},{"name":"CUDA","optional":true},{"name":"DPO","optional":true},{"name":"ExecuTorch","optional":true},{"name":"GPTQ","optional":true},{"name":"GRPO","optional":true},{"name":"Hugging Face","optional":true},{"name":"Isaac Sim","optional":true},{"name":"llama.cpp","optional":true},{"name":"MLFlow","optional":true},{"name":"MuJoCo","optional":true},{"name":"ONNX Runtime","optional":true},{"name":"OpenVINO","optional":true},{"name":"Physical AI","optional":true},{"name":"PPO","optional":true},{"name":"Quantization","optional":true},{"name":"SGLang","optional":true},{"name":"TensorRT","optional":true},{"name":"TensorRT-LLM","optional":true},{"name":"Transformers","optional":true},{"name":"Triton","optional":true},{"name":"TRL","optional":true},{"name":"Vision-Language-Action","optional":true},{"name":"vLLM","optional":true},{"name":"Weights & Biases","optional":true}],"status":"live","first_seen_at":"2026-11-06T12:57:52Z","employer_posted_date":"2026-11-06","last_verified_at":"2026-11-07T00:52:04Z","board_verified":true,"closed_at":null,"days_open":0,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":0},"description":"Multiverse Computing\nMultiverse 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.\nOur mission is to enable organizations to gain a meaningful competitive edge through cutting-edge AI and quantum technologies.\nYou’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.\nAs a Machine Learning Engineer, you will:\nBuild 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.\n\nBuild scalable data pipelines for curation, filtering, deduplication, tokenization and synthetic data generation.\n\nBuild and maintain evaluation pipelines, so benchmark results are reproducible and comparable across models and checkpoints.\n\nBuild RL environments, verifiers and reward pipelines for reasoning, coding and agentic tasks, and keep rollouts fast and stable.\n\nProfile and optimise training and inference: GPU utilisation, memory, parallelism configuration, checkpointing, I/O.\n\nPackage, optimise and benchmark models for deployment, including on non-NVIDIA and edge hardware.\n\nTurn research ideas (new architectures, compression methods, training recipes) into clean, tested, reusable code, and run experiments together with the research team.\n\nKeep our work reproducible through containers, configs, experiment tracking, CI, code review and documentation.\n\nRequired Qualifications\nMaster’s in Computer Science, AI, Mathematics, Physics, Engineering or a related field, or equivalent industry experience.\n\n2+ years of industry experience building ML systems, including at least 1 year hands-on with transformer models or LLMs.\n\nStrong Python software engineering: clean, maintainable, tested code; git, code review, unit and integration tests, CI.\n\nStrong PyTorch and hands-on experience training or fine-tuning transformer models.\n\nExperience running multi-GPU jobs and a working understanding of distributed training (data, tensor and pipeline parallelism; FSDP or DeepSpeed).\n\nComfortable with Linux, containers (Docker or similar) and shared GPU clusters (Slurm or Kubernetes).\n\nExperience building data pipelines that process large datasets.\n\nSolid ML fundamentals: you understand what happens inside training, not just how to launch it.\n\nGood debugging and communication skills. Fluent English.\n\nPreferred Qualifications\nWe 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.\nExperience with the NVIDIA stack: Megatron-LM, Megatron Bridge, NeMo RL, NeMo Gym, NeMo Curator.\n\nHands-on post-training: SFT, DPO, or RL (GRPO, PPO), or frameworks such as verl, TRL or OpenRLHF.\n\nInference engines (vLLM, SGLang, TensorRT-LLM) for serving, synthetic data generation or RL rollouts.\n\nDeployment on edge or alternative hardware: llama.cpp, ExecuTorch, ONNX Runtime, OpenVINO, vendor NPU SDKs.\n\nModel compression: quantization (GPTQ, AWQ, FP8), pruning, distillation.\n\nGPU performance work: profiling (Nsight, PyTorch profiler), Triton or CUDA kernels.\n\nEvaluation tooling: lm-evaluation-harness, lighteval, NeMo Evaluator.\n\nExperiment tracking and MLOps (MLflow, Weights & Biases), cloud platforms (ideally AWS).\n\nExperience with Physical AI tooling: robotics simulation (Isaac Sim/Lab, MuJoCo) or vision-language-action models.\n\nSpanish.\n\nExceptional Qualifications\nMerged contributions to major open-source ML projects (PyTorch, Megatron-LM, vLLM, Hugging Face, etc.).\n\nKnowledge of tensor networks or quantum-inspired methods.\n\nPublications or patents.\n\nPerks & Benefits\nIndefinite contract.\n\nEqual pay guaranteed.\n\nVariable performance bonus.\n\nSigning bonus.\n\nWe offer work visa sponsorship (if applicable).\n\nRelocation package (if applicable).\n\nPrivate health insurance.\n\nEligibility for educational budget according to internal policy.\n\nHybrid opportunity.\n\nFlexible working hours.\n\nLanguage classes and discounted lunch options.\n\nA high-performance, collaborative environment, operating at pace on cutting-edge technologies.\n\nCareer plan. Opportunity to learn and teach.\n\nAs an equal opportunity employer, Multiverse Computing is committed to building an inclusive workplace. 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