ML Ops Engineer at Circadia Health
El Segundo, CA
—
Jun 28, 2026
El Segundo, CA
Jul 21, 2026
What this job asks for AI summary
This role centers on building and maintaining the production ML infrastructure for a clinical patient-monitoring platform, including pipeline orchestration with Apache Airflow, model deployment on AWS and edge hardware, experiment tracking via MLflow, and monitoring for data drift and model degradation. The work spans the full ML lifecycle — from training and versioning to rollout strategies and incident response — within a healthcare environment subject to HIPAA and SOC 2 requirements. It suits an engineer with hands-on MLOps or ML infrastructure experience who is comfortable operating independently in a small team.
Senior level · 4+ years · National · Full-time
“or” means any one of them counts — you don't need all of them.
We read this from the posting text with AI. Skim the description below before ruling yourself out.
How this req sits in the market our data
Rare in this occupation — lead with these, and say what you built with them.
Most people in this occupation already list these. Still required — just not what gets you shortlisted.
What the occupation pays Median $135,980 (middle half $105,210–$171,980).
Estimated from BLS employment for this occupation and area, per-skill prevalence across our listing corpus, and published wage benchmarks — as of Jul 28, 2026. It is a model, not a headcount.
Why we read it this way (7)
No work location is specified in the posting — the CBSA and state fields are left blank. The role may be remote or at Circadia Health's offices; the posting does not clarify.
The title 'ML Ops Engineer' carries no seniority level word, so advertised seniority is Unspecified. The 4+ years requirement and end-to-end ownership scope support a Senior classification.
SOC confidence is Medium: the role is primarily about building and shipping ML pipeline software (15-1252), but it has a meaningful infrastructure-operations dimension (15-1244). The code-writing emphasis (Python pipelines, IaC, CI/CD tooling, internal tooling) tips it to Software Developers.
MLflow appears in the Required Qualifications section as 'MLflow preferred' — the parenthetical 'preferred' qualifies the specific tool, not the requirement category (model registry/experiment tracking is clearly required). It is treated as a hard gate on the capability, with MLflow as the named primary tool.
Snowflake appears in Required Qualifications as 'Snowflake preferred' — same reasoning as MLflow above; the capability (SQL/data warehousing) is required, Snowflake is the named preferred tool.
Edge/embedded device deployment, healthcare/clinical data background, HIPAA familiarity, and distributed compute frameworks (Spark, Dask) are all listed under Preferred Qualifications.
No compensation figures are provided in the posting.
Read the full posting
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