Schibsted

About The Team
AI Foundations is Schibsted's central AI platform team, responsible for core services and infrastructure that enable rapid and reliable ML and AI development. Located in Sweden and Norway, the team maintains platform components for ML orchestration, feature and vector embedding, ML and LLM model serving, an LLM gateway, and an emerging agent platform. We collaborate with many parts of the organisation, including experiment teams, product development, security &amp; privacy, and journalists.
Teams across Schibsted are building agents, each with their own frameworks and approaches. We need to turn that into a shared foundation: a paved road from prototype to production that is secure, observable, and affordable to run. You’ll help build it, working with senior engineers on our team and with agent builders across the company.
What will you do in this role?
<ul>
<li>Platform and tooling: Extend and operate our agent platform built on AWS AgentCore, help teams deploy and debug, and build well-tested tools and harnesses that give agents safe access to internal code, data and services.</li>
<li>Access and governance: Help run our MCP gateway and skills registry, so agents reach only what they’re allowed to and good work gets shared across teams.</li>
<li>Features &amp; golden paths: Learn how teams build agents today, then turn what you find into platform features, standards, templates, and clear guidance on what to use when.</li>
<li>Observability and cost: Set up tracing, dashboards and per-team cost reporting for agent runs.</li>
<li>Evals and safety: Write evals for agent behaviour and tool use, and test for prompt injection and over-permissioned tools.</li>
<li>Users and ecosystem: Be the first point of contact for engineers, data scientists and journalists building agents, and turn recurring questions into docs, templates and standards.</li>
<li>Keep us current: Explore! Stay current with the fast-moving agentic ecosystem (new protocols, frameworks, managed services), try things out, and help the team separate what's worth adopting from hype.</li>
</ul>
What we're looking for
You Bring a Strong Engineering Mindset, a Structured Approach To Problem-solving, And The Ability To Collaborate With Cross-functional Stakeholders. More Specifically, You Have
<ul>
<li>0–2 years of experience as an ML, AI or software engineer. Internships, thesis work and serious projects or contributions in the open source space count.</li>
<li>Master's degree in Computer Science, Artificial Intelligence, or a related field.</li>
<li>Strong Python skills, with a habit of writing clean, tested code.</li>
<li>A practical understanding of how LLM agents work: tool calling, caching, context management, and common failure modes. You've built at least one agent or tool-using LLM application, even as a side project.</li>
<li>Comfort with Docker, Git and the command line, and with reading logs and traces to find out why something broke.</li>
<li>A clear communication style: you can explain technical solutions to non-technical colleagues, including journalists.</li>
<li>A curious and security-minded attitude. Agents that can act on internal systems raise important questions about access, data exposure and misuse, and we want someone who finds those questions interesting.</li>
</ul>
Nice to have
<ul>
<li>Hands-on experience with MCP (building servers or clients), agent frameworks such as Pydantic AI, LangGraph or similar.</li>
<li>Experience with LLM gateways (e.g. LiteLLM) and LLM observability or tracing tools (e.g. Langfuse, LangSmith, OpenTelemetry).</li>
<li>Familiarity with authentication and authorisation (OAuth/OIDC, service identities, least privilege).</li>
</ul>
Our tech stack
<ul>
<li>Cloud infrastructure: Hybrid AWS/GCP, Kubernetes, Karpenter, KEDA</li>
<li>CI/CD: Terraform, GitHub Actions</li>
<li>Agentic solutions: AWS AgentCore, MCP, Pydantic AI, Promptfoo</li>
<li>LLM solutions: LiteLLM, Bedrock, OpenWebUI</li>
<li>Model serving: Triton Inference Server, vLLM</li>
<li>Vector/Feature serving: Vespa, Feast</li>
<li>Workload orchestration: Flyte, MLflow, Ray</li>
<li>Monitoring &amp; alerting: Grafana</li>
<li>Coding: Python, Terraform, TypeScript</li>
</ul>
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 diving into new AI tools and frameworks, refining MLOps pipelines, or advancing LLM architectures, we support continuous learning and specialization.
About The Company
Independent Journalism – That’s our business
Schibsted Media Group includes some of the strongest media brands in the Nordics, including VG, Aftenposten, E24, Bergens Tidende, Stavanger Aftenblad, Aftonbladet, Svenska Dagbladet, Omni, and Podme.
Every day, nearly seven million people turn to our editorial media to stay informed, engaged, and entertained through text, audio, images, and video. The trust of our users is crucial to us. To maintain this trust, we prioritise truth, verifiability, and transparency.
Our 2,800 employees are based in Oslo, Bergen, Stavanger, Stockholm, Helsinki, Krakow, and Gdansk. We rely on all of them to succeed, through close collaboration across editorial teams, product and technology environments, and subscription and advertising units.
What began as Christian Schibsted's small printing business in Christiania (now Oslo) in 1839 has grown into one of the leading media companies in the Nordics. For nearly two centuries, our journalism has empowered people, built communities, exposed abuses of power, and strengthened democracies. Our democracies depend on independent journalism. That’s our business.

Junior ML Engineer (Agentic Systems) in AI Foundations

Junior ML Engineer (Agentic Systems) in AI Foundations

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