{"id":3686702,"url":"https://alion.io/job/multiverse-computing-senior-llm-engineer","title":"Senior LLM Engineer","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":"senior","employment_type":"full_time","work_mode":"hybrid","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":4,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"DeepSpeed","optional":false},{"name":"DPO","optional":false},{"name":"FSDP","optional":false},{"name":"GRPO","optional":false},{"name":"Knowledge Distillation","optional":false},{"name":"Kubernetes","optional":false},{"name":"LLM","optional":false},{"name":"Megatron-LM","optional":false},{"name":"Mixture of Experts","optional":false},{"name":"Model Distillation","optional":false},{"name":"Multimodal AI","optional":false},{"name":"Physical AI","optional":false},{"name":"Post-training","optional":false},{"name":"PPO","optional":false},{"name":"PyTorch","optional":false},{"name":"Quantization","optional":false},{"name":"SFT","optional":false},{"name":"SLURM","optional":false},{"name":"Synthetic Data","optional":false},{"name":"World Models","optional":false},{"name":"AWQ","optional":true},{"name":"CUDA","optional":true},{"name":"ExecuTorch","optional":true},{"name":"GPTQ","optional":true},{"name":"Hugging Face","optional":true},{"name":"llama.cpp","optional":true},{"name":"Mamba","optional":true},{"name":"NVIDIA NeMo","optional":true},{"name":"ONNX Runtime","optional":true},{"name":"OpenVINO","optional":true},{"name":"Reward Modeling","optional":true},{"name":"SGLang","optional":true},{"name":"Transformers","optional":true},{"name":"Triton","optional":true},{"name":"TRL","optional":true},{"name":"Vision-Language-Action","optional":true},{"name":"vLLM","optional":true}],"status":"live","first_seen_at":"2026-11-03T11:16:48Z","employer_posted_date":"2026-11-03","last_verified_at":"2026-11-07T00:52:04Z","board_verified":true,"closed_at":null,"days_open":3,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":3},"description":"Multiverse Computing\nMultiverse is a well-funded, fast-growing deep-tech company founded in 2019. We are the largest quantum software company in the EU and have been recognized by CB Insights (2023 and 2025) as one of the 100 most promising AI companies in the world.\nWith 180+ employees and growing, our team is fully multicultural and international. We deliver hyper-efficient software for companies seeking a competitive edge through quantum computing and artificial intelligence.\nOur flagship products, CompactifAI and Singularity, address critical needs across various industries:\nCompactifAI is a groundbreaking compression tool for foundational AI models based on Tensor Networks. It enables the compression of large AI systems--such as language models--to make them significantly more efficient and portable.\n\nSingularity is a quantum- and quantum-inspired optimization platform used by blue-chip companies to solve complex problems in finance, energy, manufacturing, and beyond. It integrates seamlessly with existing systems and delivers immediate performance gains on classical and quantum hardware.\n\nJob Overview\nWe 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.\nAs an LLM Engineer, you will\nTrain 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.\n\nDesign compression techniques (quantization, pruning, quantization-aware training, quantum-inspired tensor-network methods) and healing recipes that recover the quality of compressed models.\n\nBuild RL environments, reward functions and verifiers for reasoning, coding and agentic tasks, and extend RL methods to world models and Physical AI.\n\nResearch and prototype post-transformer and hybrid architectures (state-space models, linear attention, MoE), from small-scale ablations to scaling recipes.\n\nBuild data pipelines for curation, filtering, deduplication and large-scale synthetic data generation.\n\nDesign rigorous ablations and evaluations on current benchmark suites (reasoning, coding, long-context, instruction following, agentic) as well as internal evals.\n\nPort and optimise models for inference on various accelerators and edge chips, profiling latency, memory and throughput.\n\nWrite up experiments and results as internal reports and, where relevant, papers and open-source releases.\n\nReview code, mentor teammates, and follow and bring in the latest research.\n\nRequired Qualifications\nMaster’s or PhD in Computer Science, AI, Mathematics, Physics or a related field, or equivalent industry experience.\n\nSenior: 4+ years of hands-on experience training or post-training transformer models.\n\nStrong PyTorch. You are comfortable working below library level: model code, custom losses, parallelism configuration.\n\nExperience with distributed training (FSDP, DeepSpeed or Megatron-LM; data, tensor, pipeline and expert parallelism) on multi-node clusters with Slurm/K8s.\n\nHands-on post-training experience: SFT, DPO, and at least one RL method (PPO, GRPO or similar).\n\nSolid mathematical foundations in linear algebra, optimisation and probability, and an understanding of training dynamics.\n\nGood understanding of GPU fundamentals: memory hierarchy, mixed precision, profiling, throughput.\n\nRigorous experimental practice: ablation design, reproducibility, experiment tracking, noise-aware evaluation.\n\nStrong 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 have deep experience in some and none in others, we still want to hear from you.\nExperience with the NVIDIA stack (Megatron-LM, Megatron Bridge, NeMo RL, NeMo Gym, NeMo Curator) or similar frameworks (verl, TRL, OpenRLHF).\n\nRL infrastructure: rollout engines (vLLM, SGLang), asynchronous RL, reward model and verifier design, agentic environments.\n\nModel compression: post-training quantization and QAT (GPTQ, AWQ, FP8/FP4), pruning, distillation, low-rank or tensor-network methods.\n\nPost-transformer architectures: SSMs/Mamba, linear attention, hybrid models, diffusion LMs.\n\nPhysical AI: vision-language-action models, world models (e.g. JEPA-style or video world models), robotics simulation.\n\nMultimodal model training (vision-language, audio).\n\nInference on alternative hardware: ExecuTorch, llama.cpp, ONNX Runtime, OpenVINO, vendor NPU SDKs, custom kernels (Triton, CUDA).\n\nLarge-scale data processing and synthetic data generation.\n\nEvaluation tooling (lm-evaluation-harness, lighteval, NeMo Evaluator) and familiarity with Artificial Analysis.\n\nPublications at top venues (NeurIPS, ICML, ICLR, ACL, etc.), notable open-source contributions, or models released on Hugging Face.\n\nSpanish.","description_format":"text","description_chars":5317,"description_truncated":false,"requirements":{"experience_years_min":4,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":{"level":"master","optional":false},"security_clearance":false,"languages":[{"language":"English","level":"Advanced (C1)","optional":false}]},"benefits":[],"hiring_locations":[{"name":"Spain","iso":"ES","kind":"country"}],"hiring_excludes":[],"relocation_offered":false,"industries":["Machine Learning","Quantum Science & Computing","Financial AI"],"lifecycle":[{"event":"open","at":"2026-11-03T15:10:20Z"}],"visa":[],"liveness":{"score":90,"band":"hot","label":"Hiring now","p_open":1,"p_active":0.903,"p_room":1,"age_days":2,"expected_fill_days":58,"reasons":["conf:5","velocity","win:early"],"computed_at":"2026-11-06T05:58:05Z"},"pay":null,"html_url":"https://alion.io/job/multiverse-computing-senior-llm-engineer","json_url":"https://alion.io/job/multiverse-computing-senior-llm-engineer.json","meta":{"generated_at":"2026-11-07T05:20:15Z","cache_seconds":300,"methodology":"https://alion.io/methodology","terms":"https://alion.io/terms","contact":"https://alion.io/contact","api":"https://alion.io/developers","about":"Alion is a live layer of people, companies and AI agents: who they are, whether they are real and active right now, what they do and how to work with them, readable by people and by agents and paid per call.","catalog":"https://alion.io/catalog.json","usage":{"tier":"crawler_verified","counted_by":"address","units_charged":1,"used_today":12959,"day_limit":null,"remaining_today":null,"minute_limit":300,"resets_at":"2026-11-08T00:00:00Z"}},"offers":[{"id":"company.slices","title":"One company in depth, by slice","status":"live","price":{"credits":0.02,"usd":0.002,"plus_per_slice":{"credits":0.05,"usd":0.005}},"unit":"per company, plus each slice with data","note":"the employer in depth","call":{"mcp_tool":"get_company","arguments":{"id":1535},"rest":"https://alion.io/mcp/rest/get_company?id=1535"},"human":"https://alion.io/catalog?offer=company.slices&for=job%2Fmultiverse-computing-senior-llm-engineer"},{"id":"market.stats","title":"A market slice: pay, demand and time to fill","status":"live","price":{"credits":1,"usd":0.1},"unit":"per slice","note":"pay, demand and time to fill for this role and place","call":{"mcp_tool":"market_stats"},"human":"https://alion.io/catalog?offer=market.stats&for=job%2Fmultiverse-computing-senior-llm-engineer"},{"id":"job.search","title":"Open jobs by role, technology, place, pay and visa","status":"live","price":{"credits":0.02,"usd":0.002},"unit":"per posting in a list","note":"similar open postings","call":{"mcp_tool":"search_jobs"},"human":"https://alion.io/catalog?offer=job.search&for=job%2Fmultiverse-computing-senior-llm-engineer"},{"id":"company.verify","title":"Is this company real and active right now","status":"pilot","price":null,"unit":"per company","request":{"url":"https://alion.io/catalog/request","method":"POST","body":"{\"offer\": \"company.verify\", \"for\": \"job/multiverse-computing-senior-llm-engineer\", \"note\": \"what you need it for\"}"},"human":"https://alion.io/catalog?offer=company.verify&for=job%2Fmultiverse-computing-senior-llm-engineer"}],"agent":{"mcp":"https://alion.io/mcp/jobs","mcp_open":"https://alion.io/mcp/open","next":[{"tool":"get_job","arguments":{"id":3686702}},{"tool":"search_jobs","arguments":{"company":"multiversecomputing.com","detail":"compact"}},{"tool":"match_people","arguments":{"job_id":3686702},"note":"people open to offers who fit this vacancy"}],"note":"The same data through MCP tools: https://alion.io/mcp/open needs no sign-in (compact searches, postings, people); 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