remote

Salary
Posted
Jul 21, 2026
Location
Last confirmed open
Jul 23, 2026

What this job asks for AI summary

A hybrid data engineering and applied ML role focused on building risk detection systems using classical ML, LLMs, and generative AI techniques within a SaaS platform. The position spans the full stack — from designing and maintaining data ingestion pipelines and warehousing layers, to training and deploying models via production APIs, to owning MLOps infrastructure and measuring real-world business outcomes. Suits someone equally comfortable with data engineering and ML research who has a track record of shipping models to production.

Senior level · 5+ years · Remote · Full-time

Must have (11)
PythonSparkSQLDatabricks or Snowflakedbt, Databricks Autoloader or InformaticaMLflow or Weights BiasesGitCI/CDTerraformDockerApache Airflow or Databricks Workflows
Nice to have (2)
LLMsRAG

“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

Roughly 630 people nationally plausibly meet what this posting asks for (data scientists). range 380–1,150

Applicant volume Moderate — A normal amount of company. The rare requirements below are what will separate a shortlisted application from the rest.

What gives you an edge
dbt4%MLflow8%Terraform11%

Rare in this occupation — lead with these, and say what you built with them.

What won't set you apart
Python88%SQL72%Docker53%CI/CD45%

Most people in this occupation already list these. Still required — just not what gets you shortlisted.

What the occupation pays Median $122,874 (middle half $87,544–$162,374).

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 (6)

This role is a genuine hybrid of data science/ML and data engineering — roughly equal weight is given to both disciplines. 15-2051 (Data Scientists) was chosen as primary because the job description leads with ML/LLM model development and production ownership; 15-1243 (Database Architects) is a credible runner-up given the substantial data pipeline and warehouse design responsibilities.

The posting states 'flexible work options' and 'we cannot support international remote work', implying US-based remote is permitted. No specific city or metro is mentioned.

The experience requirement is stated as '5–8+ years spanning data engineering and data science/ML'; 5 years is used as the minimum.

LLMs, Generative AI, and RAG appear under a 'Strong Plus' section, which functions as a preferred/nice-to-have block rather than a hard gate.

The dbt alternatives field also includes 'Lakeflow Declarative Pipelines' (a Databricks product) per the JD's 'or similar' framing; Informatica and Databricks Autoloader are named explicitly alongside dbt in the same requirement.

No compensation figures are provided in the posting.

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