Machine Learning Engineer at Sift
San Francisco, CA
$140,000–$190,000
Jul 19, 2026
San Francisco, CA
Jul 21, 2026
What this job asks for AI summary
A fraud-detection platform role focused on building and maintaining end-to-end machine learning pipelines — from feature engineering on large volumes of behavioral event data to training per-merchant models and serving predictions at low latency in production. The work spans model development (ensemble methods, deep learning, graph-based approaches), MLOps infrastructure, and distributed systems, and suits engineers with 4+ years of production ML experience and strong skills in Java or Scala alongside Python.
Mid level · 4+ years · Remote · Full-time
“or” means any one of them counts — you don't need all of them.
Posted 3 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 7,200 people nationally plausibly meet what this posting asks for (software developers). range 3,050–13,000
Applicant volume Light — Few people clear these requirements, so an application that does clear them gets looked at. Worth applying to even if you miss a nice-to-have.
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 Jul 28, 2026. It is a model, not a headcount.
Why we read it this way (8)
The title 'Machine Learning Engineer' carries no explicit seniority level, so the title states no level. The 4+ years requirement and scope (end-to-end pipelines, production MLOps, cross-functional collaboration) support a Mid band — the role is hands-on delivery within a team rather than org-wide technical authority.
Java and Scala are listed as interchangeable for the production backend ('Java or Scala'); Python is a separate, independently required skill.
Apache Spark is the primary named framework; Apache Flink and Hadoop are listed as alternatives ('like Apache Spark, Apache Flink, or Hadoop').
XGBoost, Random Forests, and Neural Networks are listed as examples of required ML algorithm knowledge; they are grouped as alternatives under XGBoost as the primary named example.
GCP is inferred as a hard requirement from the explicit mention of 'distributed, multi-tenant cloud environment (GCP)' in the Requirements section.
Fraud/risk/cybersecurity domain experience, Apache Kafka, Docker, Kubernetes, and AI coding assistants (e.g., Claude Code) all appear under the 'Bonus Points (Preferred Qualifications)' heading and are marked preferred.
No compensation range is stated in the posting.
Caller marked this a fully-remote role — scored against the national candidate pool.
Read the full posting
The employer publishes the full description on their own site — read it there ↗. Or sign in to read it here — it's free, and it also lets you track this application.