About the team

Schibsted's AI Foundations team is looking for a machine learning engineer to help advance AI across the company, facilitating AI experiments and development.

Located in Sweden and Norway, the team is responsible for building and maintaining core AI infrastructure and ML platform services for the company, as well as supporting the development of AI models and solutions. We collaborate with many other parts of the organisation to enable new AI initiatives, including experiment teams, product development, security & privacy, as well as journalists.

What will you do in this role?

You’ll play an important role in shaping our AI platform offering. Specifically, you will:

• Work together with product teams to develop and augment our vector store products to serve article, podcast, video, or user embeddings for personalisation and search.
• Partner with AI experiment and data infrastructure teams to develop LLM agents, RAG solutions and other pipelines that interact with our own data products
• Develop and deploy novel models for Schibsted's recommender systems, text-to-speech or video.
• Monitor, maintain, and build out our hybrid infrastructure to serve internal (LLM, RL, etc.) models cost-effectively.
• Refine our batch ML workload orchestration platform together with our users.
• Develop self-serve API solutions for accessing external LLM providers and internal LLM endpoints.
• Support experimentation and testing of new AI solutions facing millions of users (e.g., Hej Aftonbladet).
• Contribute to AI operations: setting up dashboards and alerts for our infrastructure and models.
• Contribute to a shared understanding of ML/AI knowledge and expertise and promote machine learning literacy across the company.
• Challenge and inspire the team by staying current with the latest advancements in machine learning and related technologies.

What we’re looking for

You bring a strong engineering mindset, a structured approach to problem-solving, and the ability to collaborate effectively with cross-functional stakeholders. More specifically, you have:

• 2-5 years of proven experience as a machine learning engineer, or equivalent.
• Master's degree in Computer Science, Artificial Intelligence, or a related field.
• Solid understanding of machine learning lifecycle and fundamentals, and are up-to-date with recent LLM advancements.
• Expertise in Python.
• Familiarity with common development tools like GitHub and GitHub Actions, Copilot.
• Passion for delivering products with a great developer experience.
• A clear and compelling communication style - able to translate complex engineering solutions to non-technical audiences, including journalists.

Bonus qualifications:

• Experience with embedding models, and/or vector stores such as Vespa or Weaviate.
• Experience with training (Pytorch) and serving LLM models, generative AI, and RAG applications.
• Comfortable working with multi-cloud setup and experience with AWS and/or GCP and containerised environments like Kubernetes in combination with Terraform.
• Experience working in media, content, or digital product environments.

Our tech stack:

• Cloud infrastructure: Hybrid AWS/GCP, Kubernetes
• CI/CD: Terraform, Github Actions
• Model serving: Triton Inference Server, vLLM
• Vector/Feature serving: Vespa, Tecton
• Workload orchestration: Flyte, Ray
• LLM access: OpenWebUI, LiteLLM
• Monitoring & alerting: Grafana
• Coding: Python, HCL, Javascript/Typescript

Why we enjoy working here:

At Schibsted, we value openness, curiosity, and collaboration. You’ll be part of a diverse, inclusive workplace that encourages bold thinking and continuous learning. Our work directly supports public interest journalism—and we take that responsibility seriously.

Our engineering culture is built on trust and autonomy. You’ll have significant freedom to choose your tools, influence how we work, and explore the technologies you find most compelling. Whether you're passionate about advancing LLM architectures, refining ML Ops pipelines, or diving into new AI tools and frameworks, we support continuous learning and specialization.