Staff Engineer - ML Infra / MLOps
Quince · Palo Alto, CA
$218,000–$285,000
Jul 22, 2026
Palo Alto, CA
Jul 23, 2026
What this job asks for AI summary
A senior individual-contributor role focused on building and owning the end-to-end ML platform at a retail/e-commerce company — covering distributed training pipelines, feature stores, real-time inference serving, and the developer tooling that lets data scientists ship models to production. The position also carries responsibility for GPU cost optimization, deployment reliability, and technical mentorship of junior engineers.
Staff level · 8+ years
Pay in the description: $218,000–$285,000
“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
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). 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 (8)
The job title is 'Staff Engineer for ML Infra / MLOps' and the role's scope — setting cross-team architectural direction, owning the full ML platform, and mentoring junior/mid engineers — genuinely supports the Staff band, not merely Senior.
Location is not stated in the posting; no CBSA or remote status could be confirmed. Remote is marked false by default given the absence of any remote language, but this is uncertain.
AWS is listed as 'preferably AWS' (a substitution qualifier within a required qualifications block), so it is treated as a hard gate on cloud-native infrastructure with AWS as the preferred option; GCP and Azure are listed as alternatives.
Terraform and Pulumi are listed together as 'Terraform/Pulumi' in the required qualifications block — one skill with the other as an alternative, since the posting treats them as interchangeable IaC options.
PyTorch and TensorFlow are listed as interchangeable ML framework options ('such as PyTorch, TensorFlow, Kubeflow, or SageMaker'); PyTorch is emitted as primary with TensorFlow as alternative. Kubeflow and SageMaker are emitted as a separate skill pair given they serve a distinct MLOps/orchestration function.
EKS (Amazon Elastic Kubernetes Service) appears in the qualifications block alongside Kubernetes but is a specific managed-service sub-product; it is marked preferred since the hard gate is clearly on Kubernetes broadly, with EKS as the AWS-specific flavor.
The compensation range contains a likely typo ('$285.000' using a period as a decimal separator); interpreted as $285,000 USD annual.
The alternative occupation code 15-1243 (Database Architects) is noted as a runner-up given the significant emphasis on feature store design, data pipeline architecture, and data platform engineering — though the primary day-to-day work is building ML infrastructure software, which aligns more closely with 15-1252.
Read the full posting
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