Machine Learning Engineer - Backend
EU
Engineering / R&D – Machine Learning /
Full-time /
Hybrid
Neko Health is a Swedish health-tech company co-founded in 2018 by Hjalmar Nilsonne and Daniel Ek. Our vision is to create a healthcare system that can help people stay healthy through preventive measures and early detection. Neko has developed a new medical scanning technology concept to make it possible to do broad and non-invasive health data collection that is both convenient and affordable for the public. This requires completely reimagining the healthcare experience and incorporating the latest advances in sensors and AI. We are a remote first company, but the company is based in Stockholm and has over 300 employees across Europe.
About the Role.
We are looking for a Machine Learning Engineer with focus on Backend services to join our Data Science Platform team. As a Machine Learning Engineer at Neko, you will enable robust, reliable and responsible Machine Learning workflows using state of the art approaches, high volumes of data from proprietary sensors and devices. In this role, we use the following technologies: PyTorch, MLFlow, Dagster, FastAPI, Databricks and Azure.
You will work closely with Clinical Researchers, Data Scientists, Machine Learning Engineers and Data Engineers contributing to all different types of Machine Learning use cases acrossSkin, Cardio and more.
Responsibilities:
- Build reusable components for Machine Learning operations
- Own Machine Learning systems and platforms
- Optimize backend performance for real-time ML inference workloads
- Collaborate with Platform and DevOps teams to ensure backend systems are robust, monitored, and fault-tolerant
- Ensure compliance with healthcare and data privacy regulations in all backend designs
- Create business-critical inference workflows delivering results to Neko members in a reliable and timely manner
Requirements:
- Strong programming skills in Python and experience with backend frameworks (e.g., FastAPI)
- Advanced knowledge of production machine learning tools and practices
- Deep understanding of distributed systems, microservices architecture, and API design
- Familiarity with ML lifecycle tools (MLFlow, Kubeflow, etc.) and orchestration frameworks (Dagster, Airflow)
- Experience with containerization and infrastructure tools (Docker, Kubernetes, Terraform)
- Comfortable working with cloud platforms (preferably Azure) and managing CI/CD pipelines
- Ability to navigate through complex systems including medical domain, regulations, firmware, hardware and sensors
- Skilled in navigating a constantly evolving ecosystem of tools and platforms, distilling the relevant recommendations to a specific context
About the Engineering Team
Distributed and Remote First
We are nearly 100 full time engineers at the company, working from Berlin, Chamonix, Hamburg, Lisbon, Marseille, Vilnius, and Stockholm, spanning diverse disciplines such as Hardware Engineering, Firmware Development, Electrical Design, Algorithm Development, Machine Learning Development, Optronics Research, Frontend Development and more. We don't expect people to join us with a specific tech knowledge, but we do expect you to work with our tools. We use a mix of React, Typescript, C++, and Python. Our APIs are written in C# with ASP.NET Core, uses Azure Cosmos DB, and Azure Active Directory for authentication.
Our headquarters and our hardware development team are in Stockholm, Sweden.
We are a Remote First company; however, it is of course much easier to work remotely as a software engineer than a hardware or firmware engineer (since they require access to hardware or devices occasionally). Software engineers based in Stockholm work maybe one day a week or one day every two weeks from the office.
We meet a couple of times per year to get to know each other and have fun.
Organization and Way of Working
The engineering team is divided into smaller cross functional project teams that each focus on a specific goal or target, where some groups are long-lived, and some are short-lived, depending on how big the goal or deliverable is. We strive to create groups which are cross-functional and able to complete their goals without dependence on other teams, even though this is of course not always possible.
Groups track goals on a yearly and quarterly basis with goal follow-up across the entire engineering organization on a bi-weekly basis. Most groups do internal planning on a bi-weekly basis, but in the end it's up to the group to decide how they want to work.
We have, however, mandated that all groups must present their progress or failures or hacks at our bi-weekly engineering demo, a fun meeting/presentation where we talk about everything from short-circuiting power-modules, how hard it is to calibrate cameras or align polygons in space, to neat new command line tools for operations, a new auth mechanism in the backend, a cool new way to visualize health data or a new feature which helps our doctors be more productive.
We have a flexible workplace that focuses on work/life balance, and we strongly believe in our mission but do not think that achieving it requires sacrificing everything else.
