Senior Staff Machine Learning Platform Engineer
Faire · San Francisco, CA
$295,000–$405,500from the description
Jul 1, 2026
San Francisco, CA
Jul 21, 2026
What this job asks for AI summary
A senior individual-contributor role focused on owning and evolving the architecture of a wholesale marketplace's machine learning platform — covering training, inference, feature management, MLOps, and model lifecycle. The work involves setting org-wide technical standards, leading cross-functional platform initiatives, and working across Databricks, Spark, and related big-data tooling to support multiple data science teams at scale. Suits a seasoned ML infrastructure engineer with strong distributed-systems depth and a track record of technical leadership.
Staff level · 10+ years · San Francisco-Oakland-Berkeley, CA · Full-time
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 3 people in the San Francisco-Oakland-Berkeley, CA area plausibly meet what this posting asks for (data scientists). range 1–5
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 $173,851 (middle half $133,298–$217,653). 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 (7)
SOC classification is a genuine judgment call: this role is titled 'ML Platform Engineer' and is primarily about architecting and building ML infrastructure systems (pipelines, feature stores, training/inference platforms) rather than doing data science or statistical modeling. 15-1252 (Software Developers) and 15-1243 (Database/Data Architects) were also considered; 15-2051 was chosen because the core domain is ML platform ownership and the role is explicitly embedded in the data science org, but a reasonable case exists for 15-1252 given the heavy systems-building emphasis.
The degree requirement states 'preferably graduate level' — this is framed as a preference, not a hard gate, so the degree requirement is set to None.
Python, SQL, Databricks, Unity Catalog, Spark, MLflow, Delta Lake, and AWS are marked required because they appear in the 'What it takes' requirements section with firm language ('deep expertise', 'strong background', 'proficient with') or are explicitly called out as core to the role's technical scope.
All other technologies (PyTorch, PySpark, Kafka, Snowflake, Airflow, Kubernetes, Docker, Terraform, GitHub Actions, SageMaker, S3, Datadog, Kotlin, Fivetran, Iceberg, MySQL) appear only in the 'Tech Stack' table under 'you'll want to be proficient with' — a stack-context framing rather than a hard gate.
LLM workflow integration is explicitly called 'a plus' in the requirements section.
The role is hybrid (3 days/week in-office in San Francisco), not fully remote. Up to 4 weeks/year remote flexibility is mentioned but does not make the role remote.
The title 'Senior Staff' is treated as Staff-level; the actual scope (org-wide architecture, cross-functional technical leadership, setting company-wide standards) clearly supports Staff rather than Senior.
Read the full posting
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