{"id":3868036,"url":"https://alion.io/job/ericsson-llm-post-training-engineer","title":"LLM Post-Training Engineer","company":{"id":56456,"name":"Ericsson","domain":"ericsson.com","url":"https://alion.io/company/ericsson","size_band":"5000+","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Eightfold","truth_index":{"grade":"A","score":85,"open_postings":126,"ghost_share":0.024,"stale_share":0.468,"repost_share":0.103,"time_to_fill_p50_days":35,"computed_at":"2026-11-06T05:58:05Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":null,"employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Yokohama, Japan","Madrid, Spain"],"countries":["JP","ES"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":null,"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"DPO","optional":false},{"name":"Fine-tuning","optional":false},{"name":"GRPO","optional":false},{"name":"LLM","optional":false},{"name":"LoRA","optional":false},{"name":"Post-training","optional":false},{"name":"PPO","optional":false},{"name":"QLoRA","optional":false},{"name":"Reward Modeling","optional":false},{"name":"RLHF","optional":false},{"name":"SFT","optional":false},{"name":"Synthetic Data","optional":false},{"name":"PEFT","optional":true}],"status":"live","first_seen_at":"2026-07-10T03:15:33Z","employer_posted_date":"2026-11-06","last_verified_at":"2026-11-07T02:57:06Z","board_verified":true,"closed_at":null,"days_open":120,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":120},"description":"Join our Team\nAbout this opportunity\nThe AI Accelerator within BNEW SAN is on a mission to innovate, incubate, and deliver AI-based solutions that fundamentally redesign Customer Support and Network Deployment - shifting from human-led coordination to composable, agent-centric intelligence. We aim to move beyond simple efficiency gains to create a compounding cycle of optimization, where every interaction generates actionable insights that continuously refine operations, reduce time-to-market, and accelerate the journey toward autonomous network operations.\nWe are looking for an LLM Post-Training Engineer to own the process of making foundation models genuinely useful in our domain. Pretrained LLMs are powerful starting points - but it is post-training that transforms a general-purpose model into a reliable, domain-aligned agent capable of operating autonomously in the complex world of telecommunications. You will design and execute the training strategies, reward frameworks, and data pipelines that shape how our models reason, respond, and improve - closing the gap between what a model knows out of the box and what our agents need to do in production.\nWhat you will do:\nDomain Knowledge Injection via Fine-Tuning\nDesign and execute supervised fine-tuning (SFT) pipelines that inject domain-specific knowledge into base LLMs across Customer Support and Network Deployment contexts\nApply parameter-efficient fine-tuning techniques - including LoRA, QLoRA, and adapter-based methods - to adapt large models effectively within compute and latency constraints\nCollaborate with the AI & Domain Specialist and AI Data & Integration Engineer to ensure fine-tuning datasets are high quality, domain-representative, and correctly formatted for training objectives\nMaintain a structured catalog of fine-tuned model variants, adapter checkpoints, and associated training configurations to support reproducibility and lifecycle governance\nPolicy-Reward Training & Alignment\nDesign and implement policy-reward training pipelines using advanced optimization methods including GRPO (Group Relative Policy Optimization) and SAPO (Soft Adaptive Policy Optimization)\nDefine reward models and preference datasets that encode domain-specific quality signals - going beyond generic helpfulness to capture what correct, safe, and operationally appropriate behavior looks like in telco contexts\nIterate on alignment strategies based on evaluation feedback and production signal - continuously refining model behavior to better match system requirements and domain acceptance criteria\nStay current with the rapidly evolving post-training research landscape, identifying and assessing new alignment techniques for potential adoption\nAutomatic Dataset Curation & Synthetic Data Generation\nBuild and maintain automated pipelines for domain dataset curation - sourcing, filtering, deduplicating, and formatting data at scale for both SFT and reward training\nDesign synthetic data generation strategies that augment real-world datasets - producing diverse, high-quality training examples for scenarios where real data is scarce, sensitive, or imbalanced\nCollaborate with domain experts to validate synthetic data quality and ensure generated examples accurately reflect real-world telco operational scenarios\nImplement data quality gates and automated checks that maintain dataset integrity throughout the curation and generation pipeline\nTraining Run Management & Experiment Tracking\nOwn the end-to-end management of training runs - including infrastructure provisioning, job scheduling, hyperparameter configuration, and run monitoring\nMaintain a rigorous experiment tracking practice, logging all training configurations, dataset versions, evaluation results, and model artifacts to ensure full reproducibility and informed decision-making\nDefine and report on key training metrics - loss curves, reward signal trends, evaluation benchmark scores, and alignment quality indicators - providing clear visibility to the AI System Architect and engineering leadership\nIdentify and resolve training instabilities, overfitting risks, and performance bottlenecks across the post-training pipeline\nYou will bring:\nProven hands-on experience in LLM fine-tuning and post-training, with a track record of adapting large language models for production use cases using SFT, LoRA, or equivalent techniques\nStrong understanding of RLHF, preference optimization, and policy-reward training methods - direct experience with GRPO, DPO, PPO, or similar approaches is highly valued\nExperience building automated data curation and synthetic data generation pipelines for LLM training at scale\nSolid grasp of experiment tracking best practices and tooling - able to maintain a disciplined, reproducible training workflow across multiple concurrent experiments\nFamiliarity with distributed training infrastructure, GPU resource management, and compute-efficient training strategies\nUnderstanding of model evaluation methodologies and how post-training decisions connect to downstream benchmark and production performance\nFamiliarity with telecommunications, RAN, or network operations domain data is a strong plus\nCollaborative and rigorous - able to work closely with data engineers, domain specialists, evaluation engineers, and architects to ensure post-training outputs meet both technical and domain quality standards\nWhy join Ericsson? At Ericsson, you´ll have an outstanding opportunity. The chance to use your skills and imagination to push the boundaries of what´s possible. To build solutions never seen before to some of the world’s toughest problems. You´ll be challenged, but you won’t be alone. You´ll be joining a team of diverse innovators, all driven to go beyond the status quo to craft what comes next.\nWhat happens once you apply?Click Here to find all you need to know about what our typical hiring process looks like.Encouraging a diverse and inclusive organization is core to our values at Ericsson, that's why we champion it in everything we do. We truly believe that by collaborating with people with different experiences we drive innovation, which is essential for our future growth. We encourage people from all backgrounds to apply and realize their full potential as part of our Ericsson team. 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