ML Ops Engineer

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.

Requirements

• 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
• 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 Proce

Benefits

Additional Information

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.

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