Junior ML Engineer

Junior ML Engineer (LLM Ops & Serving) in AI Foundations

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 stores; ML and LLM model serving; an LLM gateway; and supports the development of AI models and products across Schibsted. 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.

We are now looking for a junior ML Engineer to strengthen the team. You’ll help improve our LLM gateway and model serving infrastructure, working with senior engineers on our team and with developers across the company.

What will you do in this role?

Together with senior engineers in the team, you'll help run and extend the platform that gives Schibsted's teams reliable, secure and cost-efficient access to LLMs. Your responsibilities include, but are not limited to:

• Extend our LLM gateway. Establish autorouting for cost-efficient model usage, add caching, fallbacks and guardrails, improve telemetry, and expand our cost attribution. Onboard new use cases, and manage access.

• Deploy and support self-hosted LLMs. Run vLLM- or SGLang-based models on Kubernetes, in the cloud and on-premise. Try out and benchmark new models, engine settings and GPU types for quality, throughput, latency and cost. Support approved golden paths for on-device model serving with e.g. llama.cpp.

• Make usage and cost visible. Extend observability solutions and alerts to support per-team cost reports so teams can see what they use, what it costs, and when something breaks.

• Guard quality with evals. Write and maintain evals that tell us whether a gateway or model change helped or hurt, and which open weight models perform as well as proprietary ones.

• Support our users. Be a first point of contact for engineers, data scientists and journalists building with AI, and turn recurring questions into docs, templates and standards.

• Explore Contribute to a shared understanding of ML/AI knowledge across the company, challenging and inspiring the team by staying current with the bleeding edge of AI.

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:

• 0-2 years of experience as an ML engineer, AI engineer, software engineer or similar. We love to hear about internships, thesis work and serious side projects in the open source space too.

• Master's degree in Computer Science, Artificial Intelligence, or related field.

• You have an interest in LLM models, and all things that make a GPU go brrrr. You have experience running models locally, and have an opinion on which models are most relevant, tools, and recent releases across the LLM landscape.

• Strong Python skills, working knowledge of Docker, Kubernetes and the command line, and comfort reading logs and metrics to find out why something broke.

• A clear communication style: you can explain technical solutions to non-technical colleagues, including journalists.

• A curious and security-minded attitude to explore, learn, adapt, and share knowledge within the rapidly changing AI technology landscape.

Nice to have

• Hands-on experience with LLM inference (vLLM, SGLang, llama.cpp, or similar). You can explain quantisation, distillation, and caching strategies.

• Familiarity with LLM/AI gateways.

• Experience working with text, audio, and video data in a media context.

Our tech stack:

• Cloud infrastructure: Hybrid AWS/GCP, Kubernetes, Karpenter, KEDA

• CI/CD: Terraform, GitHub Actions

• Agentic solutions: AWS AgentCore, MCP, Pydantic AI, Promptfoo

• LLM solutions: LiteLLM, Bedrock, OpenWebUI

• Model serving: Triton Inference Server, vLLM

• Vector/Feature serving: Vespa, Feast

• Workload orchestration: Flyte, MLflow, Ray

• Monitoring & alerting: Grafana

• Coding: Python, Terraform, 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