Software Engineer, Systems ML Engineering
Meta · Bellevue, WA
$183,997–$257,000from the description
Jul 20, 2026
Bellevue, WA
Jul 22, 2026
What this job asks for AI summary
A staff-level engineering role focused on building and maintaining the infrastructure that runs large-scale machine learning workloads in production, including distributed training frameworks, model serving pipelines, and ML platform tooling. The work involves performance analysis, reliability engineering, and cross-team technical leadership. It suits experienced systems or infrastructure engineers with a background in distributed computing and production ML environments.
Staff level · 8+ years · San Francisco-Oakland-Berkeley, CA · 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 330 people in the San Francisco-Oakland-Berkeley, CA area plausibly meet what this posting asks for (software developers). range 85–430
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 $190,744 (middle half $167,095–$224,501). This posting is about at that midpoint.
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)
The posting does not specify a single office location; Meta's primary HQ (Menlo Park/SF Bay Area) is assumed. The role may be based at any Meta campus.
C++ and Python are listed together as 'C++, Python, or equivalent systems programming languages' — they are interchangeable gates on the same requirement; both are captured as required with each as the other's alternative.
Distributed training, model serving, and data pipeline infrastructure are listed as illustrative examples of large-scale distributed systems experience, but the overall requirement is clearly a hard gate.
Performance profiling/benchmarking/bottleneck identification is explicitly required under Minimum Qualifications; captured as a required skill rather than a named tool since no specific profiler is named.
PyTorch, GPU computing, CUDA, feature stores, experiment tracking, model registries, and feature flagging all appear exclusively under Preferred Qualifications.
Compensation is quoted as a range of $183,997–$257,000/year plus bonus, equity, and benefits.
Read the full posting
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