ML Ops Engineer

Neko's Mission

Neko is redefining what prevention means, from treating illness when it arrives, to sustaining health before it's ever  mission: make data-driven, preventative care accessible to more people, before symptoms appear.

In a single, non-invasive visit under an hour, proprietary technology and direct clinical care combine to deliver personalised, actionable insights. It's a team that thinks in 10x, not 10%. Every role here plays a part in building a world where prevention is the norm, and where your work genuinely helps people live longer, healthier lives.

Neko's mission is to help people stay healthy through preventive care and early detection. The MLOps Engineering Lead owns the ML infrastructure that translates sensor data into clinical inference results for Neko members, covering skin and cardio models today and expanding into new health domains. This role is the difference between ML that works in notebooks and ML that works in production at scale, reliably and within SLA, directly enabling the quality of insights Neko delivers to every member.

• Reusable MLOps component library adopted by Data Science and ML Engineering teams, reducing time-to-production for new models. (Chase 10X, not 10%)

• Business-critical inference workflows (skin, cardio) fully owned, monitored, and operating within defined SLAs. (Impact, not activity)

• Experiment tracking, model lifecycle, and evaluation standards documented and embedded across the Data Science Platform team, with adoption by at least two engineers. (Tech-enabled, human-centred)

• Databricks/Azure ML infrastructure right-sized, cost-tracked, and optimised for the current model estate. (Impact, not activity)

• MLOps best practices actively taught and adopted by at least two engineers who lacked prior MLOps grounding. (Member-first, always)

• Production-grade Python fluency: demonstrated experience building and owning end-to-end ML systems in production, not just contributing to them.

• ML orchestration ownership: hands-on experience with Dagster or equivalent, including design decisions, not just configuration.

• Kubernetes and containerised workload management in a production context.

• Infrastructure as Code:
Terraform or equivalent, applied to ML infrastructure.

• Distributed systems and data pipeline design across multiple data sources or domains.

• Experiment tracking and model lifecycle management: MLFlow or equivalent, embedded into team workflows.

• Strong communication: able to teach MLOps concepts to Data Scientists and Engineers without an MLOps background, and make architectural recommendations across engineering and science teams.

• Experience with PyTorch model serving and inference optimisation at production scale.

• Familiarity with Azure ML, AKS, and Blob Storage in an integrated ML platform context.

• Prior work in regulated environments: medical devices, ISO 13485, or equivalent.

• Exposure to computer vision or sensor data pipelines (skin, cardio, LiDAR, DICOM).

• Experience onboarding non-MLOps engineers onto platform tooling.

Distributed and Hybrid

We have nearly 160 full-time engineers working across our hubs in Stockholm, London, and Berlin, spanning disciplines including Hardware Engineering, Firmware Development, Electrical Design, Algorithm Development, Machine Learning, Optronics Research, and Frontend Development. We don't expect you to join us with specific tech knowledge, but we do expect you to work with our tools:
React, Type Script, C++, and Python. Our APIs are written in C# with ASP.NET Core, using Azure Cosmos DB and Azure Active Directory for authentication.

Our headquarters and hardware development team are based in Stockholm. We work hybrid, with engineers typically in the office 1-2 days a week. Hardware and firmware engineers need occasional on-site access to devices, so tend toward the higher end of that; software engineers have more flexibility. We come together as a full team a couple of times a year.

Engineering teams are structured into small,…