---
title: 'ML Ops Engineer at Circadia Health'
canonical: 'https://feeny.ai/job/ml-ops-engineer-circadia-health-vauxhall-gkv0sqmswkxt'
type: 'job'
last_seen: '2026-09-10'
---

# ML Ops Engineer at Circadia Health

- **Company:** Circadia Health
- **Location:** Vauxhall, London, United Kingdom
- **Employment:** full-time
- **Work type:** onsite
- **Posted:** 2026-02-23
- **Last confirmed live:** 2026-09-10
- **Apply:** https://jobs.lever.co/circadiahealth/9fb6e9aa-5c50-4814-8dce-a17ff9325bcc

## Job description

Position Overview

As an ML Ops Engineer at Circadia Health, you will own the infrastructure and operational lifecycle of the machine learning systems that power our clinical monitoring platform. You will build and maintain the production ML pipelines, deployment infrastructure, and monitoring systems that enable Circadia's predictive models to identify early signs of clinical deterioration.

Reporting to the Principal ML Engineer, you will work across ML, backend, data, and clinical teams to ensure models are reliably trained, versioned, deployed, and monitored in both cloud and edge environments. You will be a key driver in elevating Circadia's ML practice – from reproducibility and experiment tracking to CI/CD for models and operational observability.

This is a high-ownership role at a lean company where production reliability, rapid iteration, and pragmatic engineering are essential. Your work will directly impact patient outcomes by ensuring our predictive models are always running, always accurate, and always improving.

## Key Responsibilities

- Own and extend Circadia’s ML pipeline orchestration using Apache Airflow, including training, evaluation, and deployment workflows.
- Build and maintain automated pipelines for model retraining, validation, and promotion across development, staging, and production environments.
- Implement pipeline monitoring, alerting, and failure recovery to eliminate silent failures and ensure operational reliability.
- Design pipeline architectures that support rapid experimentation while enforcing production-grade reproducibility.
- Deploy and manage ML models on AWS infrastructure (e.g. AWS Batch for batch inference workloads).
- Support deployment of models to edge devices, including Circadia’s clinical monitoring hardware, working with firmware and embedded engineering teams as needed.
- Manage model versioning, promotion, and rollback workflows through the MLflow model registry.
- Evaluate and implement strategies for safe model rollouts (e.g. shadow deployments, canary releases) as the platform matures.
- Maintain and improve the MLflow-based experiment tracking and model registry infrastructure.
- Establish conventions for experiment logging, artifact storage, model metadata, and lineage tracking.
- Enable ML engineers to move seamlessly from experimentation to production deployment with minimal friction.
- Implement and maintain training data versioning and dataset management practices to ensure reproducibility of model training runs.
- Track dataset lineage, labeling provenance, and feature dependencies alongside model versions.
- Collaborate with ML engineers and data engineers to formalise dataset release and validation workflows.
- Build monitoring systems for model performance in production, including data drift detection, prediction quality tracking, and alerting on degradation.
- Implement operational dashboards for pipeline health, compute utilisation, and deployment status.
- Collaborate with data engineering to ensure upstream data quality and pipeline reliability for ML feature inputs.
- Develop incident response procedures and runbooks for ML system failures.
- Manage and optimise AWS compute resources (Batch, EC2, or similar) used for model training and inference.
- Design infrastructure-as-code solutions for reproducible ML environments.
- Drive cost optimisation across ML compute, storage, and data transfer.
- Support Snowflake integrations for feature generation and training data pipelines.
- Introduce and champion ML engineering best practices including CI/CD for models, automated testing for ML pipelines, and reproducible training workflows.
- Build internal tooling and templates that accelerate the ML development-to-production cycle.
- Document operational processes, architecture decisions, and onboarding materials for the ML platform.
- Participate in architecture discussions and technical planning to ensure ML systems scale with Circadia’s growth.
- Ensure all ML pipelines and infrastructure meet healthcare security and privacy requirements, including HIPAA and SOC 2.
- Apply best practices for handling Protected Health Information (PHI) in training data, model artifacts, and inference outputs.
- Maintain audit trails for model decisions, data access, and deployment history.

Required Qualifications

- 4+ years of experience in MLOps, ML Engineering, DevOps, or a closely related infrastructure role.
- Strong proficiency in Python for ML pipeline development, tooling, and automation.
- Hands-on experience with ML pipeline orchestration tools, particularly Apache Airflow.
- Experience with model registries and experiment tracking platforms (MLflow preferred).
- Experience deploying and operating ML workloads on AWS (Batch, EC2, S3, IAM, CloudWatch).
- Solid understanding of the ML lifecycle: training, evaluation, deployment, monitoring, and retraining.
- Experience with containerisation (Docker) and infrastructure-as-code.
- Proficiency with Git and version control workflows.
- Familiarity with SQL and data warehousing platforms (Snowflake preferred).
- Experience implementing monitoring, logging, and alerting for production systems.
- Strong debugging and incident response skills for complex distributed systems.

## Preferred Qualifications

- Experience deploying models to edge or embedded devices.
- Background in healthcare, medical devices, or clinical data systems.
- Familiarity with model serving frameworks (e.g., TorchServe, TF Serving, Triton, or custom solutions).
- Experience with CI/CD systems for ML (e.g., GitHub Actions, Jenkins, or similar).
- Experience with data versioning tools (e.g., DVC, LakeFS, or similar).
- Experience supporting data science or ML research teams in a production context.
- Exposure to HIPAA compliance and healthcare security best practices.
- Experience with distributed compute frameworks (e.g. Apache Spark, Dask) for large-scale data processing.
- Experience with streaming or real-time inference architectures.

## What You Bring

- You take ownership of ML infrastructure end-to-end — from training pipelines to production monitoring.
- You care deeply about reliability, reproducibility, and operational excellence in ML systems.
- You have strong opinions (loosely held) on how to build a great ML platform, and you’re eager to put them into practice.
- You are comfortable working in a startup environment where you’ll wear multiple hats and move fast.
- You communicate clearly across engineering, data science, and clinical teams.
- You’re motivated by building technology that directly improves patient care.

## Why Circadia Health

Circadia Health is redefining patient monitoring through contactless sensing and AI-driven clinical insights. As we scale from tens of thousands to hundreds of thousands of monitored patients, our data infrastructure is central to everything we do.

You’ll have the opportunity to:

- Work on real-world healthcare problems with measurable patient impact
- Build data systems that power clinical-grade AI and ML
- Take ownership in a fast-growing, mission-driven company
- Collaborate with a highly skilled, multidisciplinary team

## About Circadia Health

## Company Overview
- **One-liner**: Circadia Health provides the world’s first FDA-cleared contactless patient monitoring platform that uses radar and AI to detect early signs of clinical deterioration in senior care settings.
- **Entity Type**: Private (total funding $100.4M, multiple rounds including seed, convertible notes, and product crowdfunding)
- **Headquarters**: Los Angeles, California, United States
- **Founded**: 2016
- **Founders**: Michal Maslik (CTO & Co-Founder); other co-founders not publicly named in available sources

## Core Business
- **Primary industry**: Medical Equipment Manufacturing / Healthcare Technology
- **Target customers**: B2B – skilled nursing facilities (SNFs), senior living communities, home health agencies, and healthcare providers serving elderly populations
- **Mission statement**: “Reduce rehospitalizations by helping providers see changes in their patient’s health before they worsen and disrupt treatment.” [circadia.health/about](https://circadia.health/about)

## Products & Services
- **C200 Device**: FDA-cleared contactless monitoring device that uses radar and proprietary algorithms to detect heart rate, respiratory rate, bed exits, and motion. Includes two-way audio for patient communication. [circadiahealth.com](https://circadiahealth.com/)
- **Clinical Intelligence Platform**: Cloud-based software providing predictive analytics, real-time alerts, and daily risk reports powered by AI and machine learning. Integrates with electronic health records.
- **Virtual Nurse Monitoring Service**: A team of virtual nurses reviews device data and patient records 24/7/365 to deliver personalized, predictive risk alerts, enabling early intervention.

## Market Standing
- **Valuation/Market Cap**: Not disclosed
- **Key Metric**: Total funding of $100.4M (latest reported convertible note of $100M in 2017, plus seed and earlier rounds) [linkedin.com](https://www.linkedin.com/company/circadia)
- **Notable Investors**: Village Global (led seed round, backed by Bill Gates, Jeff Bezos, Mark Zuckerberg) [apply.workable.com](https://apply.workable.com/circadia-health/)
- **Growth Signals**:
  - 30K+ patients monitored per day [circadiahealth.com](https://circadiahealth.com/)
  - 30% reduction in rehospitalizations reported
  - 78 employees (+35.6% YoY), with presence in 6 countries [linkedin.com](https://www.linkedin.com/company/circadia)
  - Active hiring: 10 open positions, job postings up 400% YoY
  - $316K yearly ROI per 100-bed SNF claimed

## Competitive Advantages
- **First-mover with FDA clearance**: Only contactless monitoring system cleared by the FDA for heart rate, respiratory rate, and motion without any body-worn sensors.
- **Proprietary radar + AI**: Hardware, algorithms, and software all built in-house, creating a vertically integrated solution.
- **Predictive, not reactive**: Platform detects changes hours to days before clinical events (e.g., CHF, COPD exacerbation, sepsis, falls).
- **Covered by Medicare**: Reimbursable in skilled nursing facilities, lowering adoption barriers.

## Strategic Focus
- **Proactive senior care**: Continue reducing hospital readmissions and fall risks through early detection.
- **Clinical evidence generation**: Hiring a Senior Scientist for Real-World Evidence and Outcomes to publish rigorous studies proving impact.
- **Scaling clinical services**: Expanding virtual nurse monitoring and telehealth NP roles to support providers.
- **Global expansion**: Already operating in the UK, Pakistan, Germany, and Qatar.

## Why Work Here
- **Culture**: Mission-driven team of engineers, scientists, and physicians focused on transforming senior care. Emphasis on diversity and inclusion. [circadia.health/about](https://circadia.health/about)
- **Work model**: Hybrid – 2-3 days per week in the El Segundo, CA office for most roles.
- **Compensation**: Transparent salary ranges (e.g., Nurse Practitioner $120K–$160K, Senior Scientist $165K–$205K) [linkedin.com](https://www.linkedin.com/company/circadia)
- **Engineering culture**: In-house development of hardware, AI, and software; opportunities to work on cutting-edge radar and machine learning technology.
- **Growth**: Rapidly scaling team (+35.6% YoY) with roles across engineering, clinical, customer success, and data.

## Sources
1. [circadiahealth.com](https://circadiahealth.com/)
2. [circadia.health/about](https://circadia.health/about)
3. [linkedin.com/company/circadia](https://www.linkedin.com/company/circadia)
4. [apply.workable.com/circadia-health](https://apply.workable.com/circadia-health/)
5. [jobs.lever.co/circadiahealth](https://jobs.lever.co/circadiahealth)

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