Data & ML Engineer

**About Podspace** Podspace is the fastest\-growing distribution and monetization platform for podcasts in the Nordics. We build the tech that enables media houses and creators to reach new heights. We are a small, talent\-dense team in Stockholm that values high velocity, ownership and friendly open collaboration without egos. **The Role** We're looking for a Data \& ML Engineer to own and evolve how we work with data at Podspace, from the pipelines that move it through our systems to the machine learning models that put it to work inside our products. This is a dual\-natured role. On one side, you'll build and improve the data infrastructure that underpins both our applications and our business intelligence. On the other, you'll apply that data through ML and AI to deliver real value to our users. We think these two sides complement each other naturally, since the person building the pipelines is best placed to see what's possible with the data. You'll own meaningful parts of the architecture and work across the full data lifecycle. This is a hands\-on role, and you'll be shipping code every week, but you'll also be one of the people setting the technical direction for data and ML as we grow. **What You'll Do** **Data Infrastructure** * Design, build and operate data pipelines for ingestion, transformation and delivery across batch and streaming workloads * Rework and modernise existing data infrastructure to be more reliable, scalable, and maintainable * Own data quality and consistency across our application databases and analytics systems * Build and maintain ETL/ELT processes that serve both product features and business reporting * Work closely with the engineering team on data modelling and how our applications interact with data * Make data accessible, well\-structured and queryable for the rest of the business **Machine Learning \& AI** * Spot opportunities to apply ML/AI inside our products and help the team prioritise them * Build, train and deploy machine learning models that integrate directly into our applications * Design and implement feature engineering pipelines that feed into ML workflows * Monitor model performance in production and iterate on improvements * Work with LLMs and other AI tools where appropriate, integrating them thoughtfully into the product experience * Stay current with developments in applied ML/AI and bring the useful parts back to the team **What We're Looking For** **Must\-haves:** * Strong track record building and operating data pipelines in production, whether batch, streaming, or both * Fluent in SQL and comfortable with both relational and analytical databases (we use PostgreSQL and ClickHouse) * Hands\-on machine learning experience, including training models, evaluating performance, and deploying to production * Solid with modern data transformation tooling (dbt, Airflow, Dagster, Prefect, or similar) * Comfortable making pragmatic trade\-offs between performance, reliability, and complexity * Able to work across the stack and collaborate closely with application developers **Strongly preferred:** * Experience with LLMs, embeddings, RAG, or other applied AI techniques shipped into real products * Background in MLOps, covering model versioning, experiment tracking, and automated retraining * Experience with event\-driven architectures, streaming systems, or high\-volume data pipelines **Nice\-to\-haves:** * Experience with TypeScript, or happy to work in a TypeScript\-heavy codebase * Familiarity with our application stack (Next.js, Fastify, PostgreSQL) * Experience with BI or data visualisation tools (e.g. Steep, Looker, Tablau) * Experience in a small team where you wear many hats and define processes as much as follow them **What We Value** We care about impact, judgement, and the ability to lift the people around you. We're looking for someone who can see the whole system, from raw events to production models, and quietly make it better. You enjoy building things well, you sweat the details that matter at scale, and you work well with others without making it about yourself.