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.

Role Purpose

As a Lead Machine Learning Engineer focused on MLOps within the Data Science Platform team, you will enable robust, reliable, and responsible machine learning workflows at scale. Working with high-volume data from proprietary sensors and devices, you will design and operate production-grade ML systems, ensuring strong experiment tracking, model lifecycle management, and scalable deployment across multiple healthcare domains.

What You’ll Deliver in the First 6–12 Months

• Build and productionize reusable MLOps components supporting scalable and reliable ML workflows.

• Establish strong ML lifecycle practices including experiment tracking, evaluation, and reproducibility.

• Enable robust and monitored ML systems aligned with healthcare-grade reliability and compliance requirements.

• Deliver reliable production inference workflows powering real-world outcomes for Neko members.

• Partner across data, platform, and clinical teams to support scalable ML adoption across multiple use cases.

Responsibilities

• Build reusable and scalable components supporting Machine Learning operations and platformization.

• Own and maintain Machine Learning systems and platform services.

• Establish and promote best practices across experiment tracking, model lifecycle, and evaluation.

• Design and maintain production inference workflows delivering reliable and timely outputs.

• Collaborate cross-functionally with Clinical Researchers, Data Scientists, ML Engineers, and Data Engineers.

• Ensure ML systems and workflows align with healthcare and data privacy requirements.

Minimum Qualifications

• Strong programming skills in Python with solid understanding of Machine Learning concepts.

• Experience building end-to-end production ML systems and platformization initiatives.

• Knowledge of PyTorch, Kubernetes, Terraform, distributed systems, and ML orchestration tools.

• Advanced understanding of production Machine Learning tools and best practices.

• Ability to operate within complex ecosystems spanning medical domain, regulatory requirements, hardware, firmware, and sensor data.

• Strong judgment navigating evolving tooling landscapes and applying the right solutions to real-world problems.

About the Engineering Team

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.

Organization and Way of Working

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.

ML Ops Engineer