Machine Learning is a growing business area for us, and we work closely with our clients to understand what actually needs to be solved, then design and build solutions that hold up in production, not just in theory.
At Playground Data, we build modern data platforms for analytics, AI and machine learning. We are a collaborative team that values solid engineering and shared growth. Our engineers take ownership and work closely with clients to deliver high-quality solutions.
What you'll actually do
You’ll work closely with our clients to understand business problems - and help resolving these by working hands on with data. Your responsibilities will stretch from the early experimentation stage to stable, production-grade systems.
The focus is true end-to-end ownership from problem framing and model design through deployment and ongoing reliability. Many of our engagements involve greenfield builds or major platform transformations.
In practice, you’ll:
- Design, build, and train machine learning models, translating real needs into working ML solutions.
- Own deployment and runtime performance in production, ensuring models are stable, monitored, and continuously improved once live.
- Establish and maintain MLOps workflows.
- Contribute internally by improving how we work with machine learning, shaping our ML practices and tooling, supporting presales when relevant, and helping Playground grow this expanding area.
You’ll never sit idle, there’s always something meaningful to build, improve, or untangle.
Who are you?
You bring at least a few years of hands-on experience in Machine Learning Engineering, and you’ve moved beyond prototypes into systems that actually run in production. Python is your core language, MLOps is a natural part of how you think, and you care just as much about deployment and operation as you do about model performance.
You would probably describe yourself as someone who:
Is technically-sound: Except for strong Python skills, you have hands-on experience with ML frameworks (e.g. PyTorch, TensorFlow, or scikit-learn). And are comfortable in the cloud (AWS, Azure, or GCP).
Thinks beyond the model: You know a good model is only half the job. You consider data quality, drift, monitoring, retraining, and how systems behave once they’re live.
Keeps calm when things are unclear: Business problems are rarely perfectly framed. You’re comfortable navigating uncertainty, iterating on hypotheses, and creating structure where there isn’t any yet.
As a person, you’re humble, genuinely curious about technology, and motivated, while knowing that asking for help is a strength, not a weakness. Friendly, curious, and playful.
You must be based in the Stockholm area and have a valid work permit for Sweden (no visa sponsorship or relocation).
Buzzword bingo: AWS, Azure, GCP, Python, PyTorch, TensorFlow, scikit-learn, MLOps, Terraform, Docker
Language requirement
Fluency in Swedish is a very-nice-to-have and English is a must. Why? Because most of our collaborations and client projects are conducted in Swedish.
Welcome to Playground!
At Playground, you work on real, complex assignments for well-established and ambitious clients, not bench projects or short-lived experiments. Our teams are trusted with production systems, platforms, and data that actually matter. Here growth comes from ownership, curiosity, and doing the work properly. You’re trusted to think, contribute, and keep getting better, one meaningful step at a time.
Playground brings together specialized domains under one umbrella, with a shared culture and way of working, and easy access to broad technical expertise when it helps move the work forward.