Hire(d) better.
Hire(d) better.
Hire(d) better.
Mission
Neko is redefining what prevention means, from treating illness when it arrives, to sustaining health before it's ever at risk. Our 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, TypeScript, 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, cross-functional groups aligned to specific goals. Some teams are long-lived while others are formed for targeted initiatives. Teams aim to operate autonomously while collaborating across the organization when necessary.
Goals are tracked quarterly and annually, with bi-weekly organization-wide progress reviews. Most teams operate on a bi-weekly planning cadence, though each group has flexibility in how they work.
All teams present progress, learnings, and experiments during bi-weekly engineering demos, covering topics ranging from hardware and calibration challenges to infrastructure improvements, backend capabilities, and data innovations that enhance clinical productivity.
Neko Health supports a flexible workplace that prioritizes work-life balance. We are deeply committed to our mission while believing meaningful impact should not require sacrificing personal wellbeing.
About titles at Neko
We use a simplified internal title framework that prioritises clarity over hierarchy, so internal titles may differ from market‑facing role titles. Scope, impact and level of the role are fully aligned and will be clearly discussed throughout the process.
Hiring Process
Candidates progress from application and structured screening through thoughtfully designed interviews culminating in a formal offer and final pre-employment checks before joining the team.
Equal Opportunity & Inclusion Statement
Neko Health is committed to inclusive hiring and member-first care. We welcome candidates from all backgrounds and encourage you to request reasonable adjustments to support your application.
Will AI replace this role?
Check AI displacement risk, transferable skills, and career alternatives for ML Ops Engineer
The ML Ops Engineer position at Neko Health is based in London. This is a hybrid position.
The ML Ops Engineer position at Neko Health is a Full time role.
Key skills for the ML Ops Engineer position at Neko Health include: azure, python, kubernetes, terraform, pytorch, machine learning, react, typescript, c++, c#.
