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About This Role
About Circadia Health
Circadia Health is a growth\-stage healthcare AI company on a mission to prevent avoidable hospitalizations and transform senior\-care operations. Our Circadia Intelligence Platform combines:
- Contactless sensing that monitors respiration and motion with medical\-grade accuracy
- Native predictive models that detect 85% of preventable adverse events several days in advance
- Enterprise integrations that operationalize predictions directly inside EHR, care\-coordination, billing, and compliance workflows
Today, our technology touches 40,000\+ post\-acute patients daily across skilled\-nursing, home\-health, and home\-care networks. We are backed by leading healthcare and AI investors and headquartered in El Segundo, CA.
### Why this role exists
Our models decide whether a care team walks into a room tonight. They train on 70,000 years of continuous vital signs joined to clinical records from more than 400,000 unique patients. When one of them degrades it does not surface as an error rate. It surfaces as a patient who deteriorated and nobody was alerted, which means the platform that trains, ships, and watches those models carries real clinical weight. You will own it: the pipelines, the path to production, and the monitoring that catches degradation before a clinician would.
### What you'll own
- Pipeline orchestration. Training, evaluation, and deployment workflows in Airflow, with automated retraining, promotion, and failure recovery.
- Deployment and release. Models onto our platform on AWS including Batch, with versioning and rollback through MLflow, maturing toward shadow and canary releases.
- Tracking and lineage. MLflow registry, conventions for artifacts and metadata, and dataset versioning so training runs are reproducible.
- Monitoring and drift. Drift, prediction quality, and degradation alerting on models where degradation is clinically consequential.
- ML compute and cost. AWS compute for training and inference, infrastructure\-as\-code, and cost optimization.
- Hands\-on model work. Contributing to model development alongside the ML engineering team, as a secondary focus behind the platform.
- Compliance. HIPAA and SOC 2 across pipelines, with sound PHI handling in training data, artifacts, and outputs.
### Required Qualifications
- 4\+ years in MLOps, ML engineering, DevOps, or a closely related infrastructure role
- Strong Python for pipeline development, tooling, and automation
- Hands\-on Airflow, and a model registry such as MLflow
- Deploying and operating ML workloads on AWS (Batch, EC2, S3, IAM, CloudWatch)
- Containerization, infrastructure\-as\-code, SQL, and Snowflake
- Building monitoring and alerting for production systems
- Enough model development experience to contribute alongside ML engineers
### Preferred
- Model serving frameworks or data versioning tools
- Healthcare, medical devices, or clinical data systems
- Significant open source, systems that outlived your tenure, or a high\-bar engineering background
As a full\-time Senior ML Ops Engineer, you will be employed by Circadia Health, Inc. The anticipated annual base salary range for this full\-time position is $150,000 \- $220,000\. The base range is determined by role and level, and placement within the range will depend on a number of job\-related factors, including but not limited to your skills, qualifications, experience, and location.
Annual salary is only one part of an employee's total compensation package at Circadia Health. We also offer:
- Meaningful employee stock options
- 100% company\-paid medical, dental, and vision coverage
- 401(k)
- Competitive time off with pay policies including vacation, sick days, and company holidays
- Impact: your work will influence care decisions for tens of thousands of seniors every day.
- Culture: hard\-working, mission\-driven, and collaborative — with weekly and monthly social events like yoga, beach bonfires, and Wednesday/Friday team lunches.
Circadia Health is an Equal Opportunity Employer. We celebrate diversity and are committed to creating an inclusive environment for all employees. All employment decisions are based on business needs, job requirements, and individual qualifications, without regard to race, color, religion, sex (including pregnancy, sexual orientation, or gender identity), national origin, age, disability, genetic information, veteran status, or any other status protected by law.
We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.
Salary Context
This $150K-$220K range is above the median for MLOps Engineer roles in our dataset (median: $168K across 34 roles with salary data).
View full MLOps Engineer salary data →Role Details
About This Role
MLOps Engineers build the infrastructure that keeps ML models running in production. They own CI/CD pipelines for model deployment, monitoring for data drift and model degradation, and the tooling that lets data scientists ship faster. If ML Engineers build the models, MLOps Engineers build the roads those models travel on.
The job is fundamentally about reliability and velocity. Data scientists want to iterate fast. Product teams want stable predictions. Your job is to make both happen simultaneously. That means building deployment pipelines that catch regressions before they hit production, monitoring systems that alert on data drift before it degrades model performance, and self-service tooling that lets data scientists deploy without filing a ticket.
Across the 4,317 AI roles we're tracking, MLOps Engineer positions make up 1% of the market. At Circadia Health, this role fits into their broader AI and engineering organization.
MLOps demand tracks closely with production ML adoption. As more companies move models from notebooks to production, the need for MLOps grows. The role is well-established at large tech companies and growing fast at mid-stage startups that are hitting the 'our models work in notebooks but break in production' phase.
What the Work Looks Like
A typical week involves: debugging a model deployment that's serving stale predictions, building a new monitoring dashboard for a feature team, writing Terraform for GPU-enabled inference clusters, reviewing pull requests for the ML platform's CI/CD pipeline, and meeting with data scientists to understand their pain points. You're the bridge between ML and infrastructure.
MLOps demand tracks closely with production ML adoption. As more companies move models from notebooks to production, the need for MLOps grows. The role is well-established at large tech companies and growing fast at mid-stage startups that are hitting the 'our models work in notebooks but break in production' phase.
Skills Required
Kubernetes, Docker, and cloud infrastructure are baseline. Most roles want experience with ML-specific tooling: MLflow, Kubeflow, Weights & Biases, or similar. Strong DevOps fundamentals matter more than ML theory. You need to understand model serving (TorchServe, Triton, vLLM), monitoring (Prometheus, Grafana), and infrastructure-as-code (Terraform, Pulumi).
GPU infrastructure knowledge is increasingly valuable as LLM inference becomes a major cost center. Understanding GPU scheduling, multi-node training setups, and inference optimization (quantization, batching, caching) puts you in the top tier. Experience with model registries and feature stores rounds out the profile.
Good MLOps postings specify their ML stack, infrastructure scale, and the problems they're solving (deployment velocity, cost optimization, monitoring gaps). Red flag: companies that want MLOps but don't have any models in production yet. You'll end up doing general DevOps instead.
Compensation Benchmarks
MLOps Engineer roles pay a median of $203,000 based on 85 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($185K) sits 9% below the category median. Disclosed range: $150K to $220K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Circadia Health AI Hiring
Circadia Health has 2 open AI roles right now. They're hiring across MLOps Engineer, AI/ML Engineer. Based in El Segundo, CA, US. Compensation range: $220K - $220K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 median).
Career Path
Common paths into MLOps Engineer roles include DevOps Engineer, Platform Engineer, Data Engineer.
From here, career progression typically leads toward ML Platform Lead, Infrastructure Architect, Engineering Manager.
DevOps engineers with ML curiosity have the shortest path. You already understand deployment, monitoring, and infrastructure. Add ML-specific knowledge (model serving, data pipelines, experiment tracking) and you're competitive. The career ceiling is high: ML Platform Lead roles at top companies pay well because the infrastructure complexity is enormous.
What to Expect in Interviews
Interviews emphasize infrastructure and reliability. Expect questions about CI/CD for ML models, monitoring for data drift, and how you'd design a model serving platform that handles 10K requests per second. Coding rounds focus on Python and infrastructure-as-code (Terraform, Helm). Be ready to discuss tradeoffs between different model serving frameworks and how you'd handle rollback when a new model degrades performance.
When evaluating opportunities: Good MLOps postings specify their ML stack, infrastructure scale, and the problems they're solving (deployment velocity, cost optimization, monitoring gaps). Red flag: companies that want MLOps but don't have any models in production yet. You'll end up doing general DevOps instead.
AI Hiring Overview
The AI job market has 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 roles).
MLOps demand tracks closely with production ML adoption. As more companies move models from notebooks to production, the need for MLOps grows. The role is well-established at large tech companies and growing fast at mid-stage startups that are hitting the 'our models work in notebooks but break in production' phase.
The AI Job Market Today
The AI job market spans 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (138) are outnumbered by mid-level (2,071) and senior (1,655) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $287,500 median, while Prompt Engineer roles sit at $145,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
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