Senior Software Engineer, ML Platform at NxT Level
San Francisco, CA
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Aug 10, 2026
San Francisco, CA
Sep 24, 2026
What this job asks for AI summary
A senior individual-contributor role focused on designing and operating a machine learning platform for a fintech company that serves small businesses. The engineer will own core infrastructure spanning real-time and batch model serving, feature stores, training pipelines, and observability tooling, working closely with data science teams to move models from experimentation into reliable production systems. Strong Python, Spark, and ML infrastructure experience are central to the role.
Senior level · 5+ years · Remote · Full-time
“or” means any one of them counts — you don't need all of them.
Posted 2 times — it's one opening, so apply once.
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 5,300 people nationally plausibly meet what this posting asks for (software developers). range 3,950–6,900
Applicant volume Moderate — A normal amount of company. The rare requirements below are what will separate a shortlisted application from the rest.
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 $138,970 (middle half $107,524–$175,762).
Estimated from BLS employment for this occupation and area, per-skill prevalence across our listing corpus, and published wage benchmarks — as of Aug 12, 2026. It is a model, not a headcount.
Why we read it this way (4)
The required tools section lists 'AWS, Databricks, MLflow, model registries, model serving, Airflow, or similar orchestration tools' as a single compound requirement with 'or similar' — this is treated as a hard gate on ML infrastructure experience, with the named tools captured individually. AWS is marked required with cloud alternatives; MLflow and Airflow are marked required as the most specifically named orchestration/registry tools in that list. Databricks appears both in the required list (as part of the 'or similar' compound) and again under Bonus Experience with deeper specificity; it is marked preferred to reflect that the bonus section is the more authoritative signal for Databricks depth.
Tecton and Feast appear only under 'Bonus Experience' and are marked preferred accordingly.
Kafka and Kinesis appear only under 'Bonus Experience' and are marked preferred accordingly.
No compensation range is stated in the posting.
Read the full posting
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