Senior Machine Learning Systems Engineer, Ads ML Experience Platform at Reddit
$216,700–$303,400
Aug 9, 2026
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Sep 24, 2026
What this job asks for AI summary
This role is on Reddit's Ads ML platform team, responsible for building the foundational infrastructure that powers machine learning development at scale. The engineer will design and ship offline experimentation platforms, production training orchestration frameworks, experiment tracking and model registry systems, and agentic AI execution platforms. It suits a backend/platform engineer with deep distributed systems experience who has also worked hands-on with ML infrastructure.
Senior level · 5+ years · Remote · 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
Roughly 5,800 people nationally plausibly meet what this posting asks for (software developers). range 1,200–12,600
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.
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 13, 2026. It is a model, not a headcount.
Why we read it this way (6)
Location is fully flexible — the posting states the role can be performed remotely from any country where Reddit has a physical office presence, so the CBSA/metro is not determinable.
The 5+ years figure is stated at the infrastructure/platform engineering level (role-level), and the 2+ years figure is specific to ML infrastructure; both are captured accordingly.
Agentic AI experience (multi-agent orchestration, MCP/A2A frameworks) is explicitly called 'a strong plus' and end-to-end model development is called 'a plus' — both treated as preferred.
Spark, Flink, and Ray are listed as interchangeable examples of distributed data processing; Kubeflow, Argo, and Airflow are listed as interchangeable orchestration options — each group is captured as one skill with alternatives.
No compensation figures are provided in the posting.
Ignored 3 non-technology phrase(s) as skills (responsibilities/concepts, not named tools): workflow orchestration, training orchestration, multi-agent orchestration.
Read the full posting
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