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My rspoven in Stockholm seeks a software engineer for its AI team. You will enhance the model that optimises buildings and build the data/platform foundation it runs on. You will overlap with Platform, Product, Customer Success and partners to ship reliable AI features.
You will own the full lifecycle from data to deployment, work with AI agents, and help scale solutions across new buildings, balancing performance with customer value.
Buildings use a huge share of the world's energy, and most of it is controlled by rules written decades ago. Myrspoven changes that. Our AI controls heating, cooling and ventilation in real time, cutting energy use and emissions while keeping people comfortable.
Our products myCoreAI and myLoadShift run in buildings today. We are a growth-stage scaleup in Stockholm, and we are now looking for a software engineer to join our AI team.
You will join our AI team, which owns the model that optimises our customers' buildings and that our whole product is built on – along with the data and systems around it. The team's mission is to make our optimisation smarter, more reliable and easier to scale to new buildings – and to make it understandable. Through visualisation and clear explanations, we help our users see what the model does, how it works and why it makes the decisions it does.
This is a software engineering role: you will improve the model itself and build the data and platform foundation it runs on. Agents write more and more of our code, so what we value most is the engineering judgement around it: framing the problem, designing the system and knowing that what we ship actually works.
You will:
Work across teams – Platform, Product, Customer Success and partners – to understand the problem before you build the solution
Share knowledge and work closely with your teammates – we keep teams small but redundant, so no one is a single point of failure
Use AI agents as a natural part of how you work – to write, test, review and ship faster
Improve the model – from data and features to training, evaluation and how it performs in real buildings
Build and run the data pipelines and platform behind our AI – ingestion, processing, orchestration, deployment and monitoring
Visualise building data and the model's decisions so they are easy to explore and understand for internal teams and for customers
Own what you build, from idea to production and beyond
A strong software engineer. You have solid experience building and running software in production. You understand how systems fit together, read code critically and know how to dig into the details when something breaks. You are at home in Linux and on the command line.
Experienced in data engineering. You have built data pipelines at scale with tools like Spark, Airflow or similar.
Self-driven with AI agents. You already use coding agents daily, know their strengths and limits, and work independently with them – steering, reviewing and verifying what they produce rather than just accepting it.
Product-oriented. You care about why something is built, not only how. You think in terms of customer value and are happy to challenge a requirement.
A clear communicator. You work well across teams, explain technical trade-offs and complex ideas simply – in words and visuals – and you ask early and keep others in the loop.
Curious. You like to understand how things work – the data, the buildings, the customers – and you keep learning as the tools change.
Ready to pitch in. We are a small company with varied needs, so you jump in where you are needed, even when it is outside your usual area.
Excited about what we do. You believe AI can make buildings far more energy efficient, and you want to help make that happen at scale.
Cloud experience, ideally Azure
Experience with time-series data or with taking ML models into production
Knowledge of buildings, HVAC or energy systems
Experience from a B2B SaaS scaleup
You don't need to know all of these from day one, but familiarity with any of them is a plus:
Language and tooling: Python, uv, Ruff, mypy, pytest
ML and optimisation: JAX, Flax, Optax, scikit-learn, SciPy
Data: Apache Iceberg, PyArrow, Polars, pandas, Spark, Kafka
Platform: Airflow, Kubernetes and Helm, Docker, Nix, Azure
Monitoring and apps: Prometheus, Grafana, FastAPI, Streamlit
Work that cuts energy use and emissions in real buildings, every day
A friendly, relaxed workplace with smart, helpful colleagues who enjoy working together
A small, senior team where your work goes straight to customers
Lots of room to shape how we build with AI – tools, ways of working and architecture
Competitive salary and stock options
Hybrid work, with at least 3 days a week at our office on Södermalm in Stockholm