{"id":2238983,"url":"https://alion.io/job/epfl-post-training-engineer-apertus","title":"Post-Training Engineer - Apertus","company":{"id":16910,"name":"Epfl","domain":"epfl.ch","url":"https://alion.io/company/epfl","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"SuccessFactors","truth_index":null},"role":"Industrial Engineering","role_family":"Industrial Engineering","seniority":null,"employment_type":null,"work_mode":"remote","remote_scope":"stated_countries","remote_scope_basis":"board_field","remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Lausanne, Switzerland"],"countries":["CH"],"hiring_countries":["CH"],"hiring_countries_total":1,"salary":null,"salary_estimate":{"min_usd":75000,"max_usd":188000,"period":"year","method":"role_country_seniority_unknown","sample_n":18},"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"DeepSpeed","optional":false},{"name":"Fine-tuning","optional":false},{"name":"HPC","optional":false},{"name":"LLM","optional":false},{"name":"Machine Learning","optional":false},{"name":"Megatron-LM","optional":false},{"name":"Post-training","optional":false},{"name":"Reinforcement Learning","optional":false},{"name":"Reward Modeling","optional":false},{"name":"SFT","optional":false},{"name":"SGLang","optional":false},{"name":"SLURM","optional":false},{"name":"Tool Use","optional":false},{"name":"TRL","optional":false},{"name":"vLLM","optional":false},{"name":"Edge AI","optional":true},{"name":"Function Calling","optional":true},{"name":"NCCL","optional":true},{"name":"Transformers","optional":true}],"status":"live","first_seen_at":"2026-09-19T02:00:00Z","employer_posted_date":"2026-09-19","last_verified_at":"2026-10-13T04:56:56Z","board_verified":true,"closed_at":null,"days_open":24,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":24},"description":"Introduction\nThe Apertus project, a joint effort between EPFL and ETH Zürich, is seeking a practical and motivated engineer to help build the next generation of open foundation models. The successful candidate will help develop and run post-training and reinforcement learning pipelines for the Apertus project.\nApertus is trained and developed on Alps, the Swiss National Supercomputing Centre’s supercomputing infrastructure. The role requires someone who is comfortable working in an HPC environment and collaborating with researchers and infrastructure engineers.\nMain duties and responsibilities\nThe engineer will contribute to the development, execution, and evaluation of scalable post-training workflows for Apertus.\nInfrastructure and systems engineering\nBuild and maintain containerized environments for LLM post-training and RL workloads.\nAdapt containers and dependencies for execution on Alps / CSCS infrastructure.\nRun and monitor Slurm-based training and evaluation jobs.\nDebug failures related to distributed execution, checkpointing, filesystem performance, networking, and GPU utilization.\nHelp maintain reproducible training recipes, configuration files, launch scripts, and documentation.\nWork with researchers and CSCS engineers to improve the reliability and performance of large-scale experiments.\nLLM post-training and Reinforcement Learning\nSupport SFT, preference optimization, and reinforcement learning workflows.\nBuild and run RL environments for tasks with verifiable outcomes, such as mathematics, code, tool-use, and reasoning.\nDevelop reward modeling, reward calibration and verifier-based training.\nGenerate and validate synthetic or gym training tasks.\nRun ablation studies comparing algorithms, reward functions, data mixtures, hyperparameters, and infrastructure settings.\nEvaluate model behavior across reasoning, coding, mathematics, instruction-following, multilingual, tool-use, and safety benchmarks.\nDebug common post-training issues, including optimization instability, reward hacking, regressions, and evaluation failures.\nProfile\nEssential\nMSc or PhD in Computer Science, Data Science, Artificial Intelligence, Machine Learning, or a related field. Exceptional BSc candidates with strong engineering experience will also be considered.\nExperience in AI and neural network architectures\nStrong collaboration and communication skills and ability to work across research and engineering teams.\nStrongly preferred\nExperience with Slurm or another HPC workload manager.\nExperience building or adapting containers for HPC or GPU clusters.\nExperience with LLM fine-tuning, post-training, preference optimization, or reinforcement learning.\nFamiliarity with distributed training concepts such as data parallelism, tensor parallelism, pipeline parallelism, checkpointing, and GPU communication.\nExperience with frameworks such as veRL, slime, Megatron-LM, DeepSpeed, TRL, vLLM, SGLang, or similar tools.\nNice to have\nExperience with RL for LLMs, online policy optimization, reward modeling, or RLVR.\nExperience creating verifiable tasks for mathematics, code, reasoning, or tool use.\nFamiliarity with lower-level GPU/distributed libraries such as NCCL, Transformer Engine, FlashAttention, or communication backends.\nExperience with large-scale evaluation pipelines.\nWe offer\nA stimulating academic environment at one of the world's leading technical universities\nThe opportunity to work with state-of-the-art supercomputing infrastructure and cutting-edge AI research\nCollaboration with top researchers and engineers from EPFL, ETH Zürich, CSCS, and other Swiss institutions\nFlexible working arrangements, including options for remote work\nProfessional development opportunities, including conference attendance and specialized training\nThe chance to contribute to open-source projects with global impact\nAccess to the broader Swiss academic ecosystem and industry partnerships\nBeing part of Switzerland's sovereign AI development, working on technology with national significance\nThe role can be based either in Lausanne at EPFL or in Zürich at ETH Zürich.\nInformations\nContract Start Date : as soon as possible\nActivity Rate Min : 80.00\nActivity Rate Max : 100.00\nContract Type: CDD\nDuration: 1 year, renewable\nReference: 2192","description_format":"text","description_chars":4265,"description_truncated":false,"requirements":{"experience_years_min":null,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":{"level":"bachelor","optional":false},"security_clearance":false,"languages":[]},"benefits":["Flexible schedule","Professional development"],"hiring_locations":[{"name":"Switzerland","iso":"CH","kind":"country"}],"hiring_excludes":[],"relocation_offered":false,"industries":["Higher Education"],"lifecycle":[{"event":"open","at":"2026-10-11T01:01:45Z"}],"visa":[],"liveness":{"score":53,"band":"ok","label":"Likely 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