## Join our Team

About this opportunity

Ericsson’s Early Career Program is an 18-month journey for you who are recently graduated or have up to five years of relevant experience. Through three rotations, including one international and market exposure, you will tackle challenges that matter, work alongside people who expect you to contribute, question, and move things forward.

The Innovate track is designed for you who are driven by building what’s next. You’ll rotate across Research, Product Development, Data, and AI contributing to solutions that don’t just support the business but move it forward.

This isn’t a programme where you sit on the sidelines. From day one, you’re hired into a role, trusted to contribute, supported to grow, and expected to make an impact. Find out more about the Early Career program here.

Your journey within the Early Career Program will start as a LLM Post-Training Engineer at BNEW SAN AI Accelerator

The AI Accelerator within BNEW SAN is transforming Customer Support and Network Deployment with agent-centric AI. As an LLM Post-Training Engineer, you’ll help adapt foundation models for telecom use cases through fine-tuning, alignment, and data work—making AI agents more reliable and useful while supporting the journey toward autonomous network operations.

## What you will do

Working with experienced engineers and domain specialists, you’ll contribute to the areas below and grow your ownership over time.

Domain Knowledge Injection via Fine-Tuning

* Contribute to developing and running supervised fine-tuning (SFT) pipelines that adapt base LLMs with domain knowledge for Customer Support and Network Deployment use cases.
* Experiment with parameter-efficient fine-tuning techniques—including LoRA, QLoRA,



and adapter-based methods—and help evaluate trade-offs in model quality, compute, and latency.
* Work with the AI & Domain Specialist and AI Data & Integration Engineer to review fine-tuning datasets for quality, domain coverage, and training-ready formats.
* Help document and organize fine-tuned model variants, adapter checkpoints, and training configurations to support reproducibility and model lifecycle management.

Policy-Reward Training & Alignment

* Support experiments with policy-reward training approaches, including methods such as GRPO and SAPO, with guidance from experienced teammates.
* Help prepare and evaluate reward signals and preference datasets that capture domain-specific quality—including correct, safe, and operationally appropriate behavior in telco contexts.
* Contribute to improving alignment strategies by reviewing evaluation results and production feedback with the team.
* Keep up with post-training research and share promising techniques for the team to assess.

Automatic Dataset Curation & Synthetic Data Generation

* Contribute to building and maintaining data-curation pipelines by helping source, filter, deduplicate, and format datasets for SFT and reward training.
* Help develop and evaluate synthetic-data approaches to create diverse training examples where real-world data is limited, sensitive, or imbalanced.




* Work with domain experts to review synthetic examples for quality and relevance to real telco operational scenarios.
* Help develop and run data-quality checks that maintain dataset integrity through curation and generation.

Training Run Management & Experiment Tracking

* Support training runs by helping configure jobs, schedule and monitor workloads, and manage GPU resources.
* Maintain clear experiment records with training configurations, dataset versions, evaluation results, and model artifacts to make results reproducible.
* Track and summarize key training metrics—such as loss curves, reward trends, benchmark scores, and alignment quality—for the AI System Architect and engineering team.
* Work with experienced teammates to investigate training instability, overfitting, and performance bottlenecks, and help test potential improvements.

## The skills you bring

We welcome early-career candidates and do not require professional experience in LLMs or model training. We are especially interested in candidates who have explored how to train large language models through academic work, research, projects, internships, or self-directed learning.

Must-have

* Foundational knowledge of machine learning, data science, or natural language processing, and a strong interest in training large language models.
* Programming skills in Python, with a careful and curious approach to experimentation, data, and model evaluation.
* A collaborative mindset and the ability to communicate ideas and learn alongside technical and domain-specialist teammates.

Nice to have

* Academic or research experience focused on large language m

📌 LLM Post-Train Eng. Early Career Program (España)
🏢 Ericsson
📍 España