About this opportunity
The 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.
We 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.
What you will do:
Domain Knowledge Injection via Fine-Tuning
- Design and execute supervised fine-tuning (SFT) pipelines that inject domain-specific knowledge into base LLMs across Customer Support and Network Deployment contexts
- Apply parameter-efficient fine-tuning techniques - including LoRA, QLoRA, and adapter-based methods - to adapt large models effectively within compute and latency constraints
- Collaborate 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
- Maintain a structured catalog of fine-tuned model variants, adapter checkpoints, and associated training configurations to support reproducibility and lifecycle governance
Policy-Reward Training & Alignment
- Design and implement policy-reward training pipelines using advanced optimization methods including GRPO (Group Relative Policy Optimization) and SAPO (Soft Adaptive Policy Optimization)
- Define 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
- Iterate on alignment strategies based on evaluation feedback and production signal - continuously refining model behavior to better match system requirements and domain acceptance criteria
- Stay current with the rapidly evolving post-training research landscape, identifying and assessing new alignment techniques for potential adoption
Automatic Dataset Curation & Synthetic Data Generation
- Build and maintain automated pipelines for domain dataset curation - sourcing, filtering, deduplicating, and formatting data at scale for both SFT and reward training
- Design 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
- Collaborate with domain experts to validate synthetic data quality and ensure generated examples accurately reflect real-world telco operational scenarios
- Implement data quality gates and automated checks that maintain dataset integrity throughout the curation and generation pipeline
Training Run Management & Experiment Tracking
- Own the end-to-end management of training runs - including infrastructure provisioning, job scheduling, hyperparameter configuration, and run monitoring
- Maintain a rigorous experiment tracking practice, logging all training configurations, dataset versions, evaluation results, and model artifacts to ensure full reproducibility and informed decision-making
- Define 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
- Identify and resolve training instabilities, overfitting risks, and performance bottlenecks across the post-training pipeline
You will bring:
- Proven 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
- Strong understanding of RLHF, preference optimization, and policy-reward training methods - direct experience with GRPO, DPO, PPO, or similar approaches is highly valued
- Experience building automated data curation and synthetic data generation pipelines for LLM training at scale
- Solid grasp of experiment tracking best practices and tooling - able to maintain a disciplined, reproducible training workflow across multiple concurrent experiments
- Familiarity with distributed training infrastructure, GPU resource management, and compute-efficient training strategies
- Understanding of model evaluation methodologies and how post-training decisions connect to downstream benchmark and production performance
- Familiarity with telecommunications, RAN, or network operations domain data is a strong plus
- Collaborative 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
Why 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.
What 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. Ericsson is proud to be an Equal Opportunity Employer. learn more.
Primary country and city: Japan (JP) || Yokohama
Req ID: 787828
Skills Required
- Hands-on experience with LLM fine-tuning and post-training for production use cases
- Experience with supervised fine-tuning, LoRA, or equivalent techniques
- Strong understanding of RLHF, preference optimization, and policy-reward training methods
- Experience building automated data curation and synthetic data generation pipelines for LLM training at scale
- Understanding of experiment tracking and reproducible training workflows
- Familiarity with distributed training infrastructure and GPU resource management
- Understanding of model evaluation methodologies and downstream production performance
- Familiarity with telecommunications, RAN, or network operations domain data
- Direct experience with GRPO, DPO, PPO, or similar policy optimization approaches
What the Team is Saying
.png)
.png)
Ericsson Compensation & Benefits Highlights
-
Retirement Support — An automatic employer contribution of 3% plus up to 4% in matching is outlined for U.S. employees, enabling a potential 7% total employer contribution subject to plan rules. Feedback suggests this is a standout element of the package.
-
Leave & Time Off Breadth — The U.S. package specifies at least 15 vacation days for new hires, up to 3 personal days, 11 holidays, and 80 hours of sick time, with flexibility noted in taking time away. Feedback suggests time‑off breadth and usability are strong points.
-
Parental & Family Support — Published materials describe up to 16 weeks of paid maternity leave and 6 weeks of paid parental or adoption leave at 100% of pay, subject to eligibility. Feedback suggests these policies provide meaningful support for families.
Ericsson Insights
What We Do
Ericsson builds the digital connectivity the world relies on. Our technology underpins the mobile networks, platforms, and systems that billions of people, businesses, and societies depend on every day. We are a global leader in communications technology, delivering mobile network infrastructure, cloud software, and wireless connectivity solutions for service providers and enterprises worldwide. Our networks support connectivity across 180+ countries, helping power everyday communication as well as critical digital services at global scale. Connectivity has evolved far beyond consumer mobile use. Today, nearly 80% of the world’s population accesses the internet via mobile networks, and Ericsson is helping shape what comes next. We are advancing 5G and 5G Advanced, developing network APIs that open connectivity to the global developer ecosystem, and applying automation and AI to make networks more intelligent, efficient, and resilient. Ericsson was the first company to launch live 5G networks on five continents, and our 5G platform is now commercially live in 150+ networks across 60+ countries. We also support more than 36,000 enterprise customers, enabling secure, high-performance connectivity for industries such as manufacturing, aviation, logistics, utilities, and public safety, where reliability and performance are mission critical. Innovation is central to how we work. Ericsson has approximately 28,000 employees in research and development, backed by one of the strongest intellectual property portfolios in the industry with 60,000+ granted patents. Our engineers, researchers, and technologists work across 100+ global R&D sites, helping define how networks evolve and how digital infrastructure is built for the long term. As the world moves toward a mobile-first, AI-powered, and cloud-driven future, connectivity becomes the foundation for digital transformation across every industry. Ericsson is building that foundation, shaping the future of digital connectivity through technology that operates at global scale and supports real-world impact, today and for what comes next.
Why Work With Us
Ericsson is a place for people who want to work on technology that powers everyday life. You’ll contribute to large-scale systems used every day, tackle complex challenges in live environments, and keep developing your skills and career in your own vision.
Gallery
.jpg)
.jpg)
.jpg)
.jpg)
.jpg)
Ericsson Offices
Hybrid Workspace
Employees engage in a combination of remote and on-site work.
Ericsson adopts a hybrid work model globally because we know balance matters. Sometimes things are better in real life. Other times we can be more productive at home. Our hybrid approach gives you the best of both worlds.
