Machine Learning Engineer, Frontier Data Products
Recruiting From Scratch · San Francisco, CA
$130,000–$500,000from the description
Jul 5, 2026
San Francisco, CA
Jul 21, 2026
What this job asks for AI summary
This role centers on building and maintaining production machine learning systems that assess and improve AI-generated outputs, with a focus on evaluation frameworks, feedback loops, and continuous learning pipelines. The work spans the full ML lifecycle — from data handling through deployment — including model monitoring, quality optimization, and integrating human review into live systems. It suits engineers with hands-on experience shipping ML systems in fast-moving, ambiguous environments, particularly those familiar with LLM applications or human-in-the-loop approaches.
Mid level · 3+ years · San Francisco-Oakland-Berkeley, CA · 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 390 people in the San Francisco-Oakland-Berkeley, CA area plausibly meet what this posting asks for (data scientists). range 290–510
Applicant volume Moderate — A normal amount of company. The rare requirements below are what will separate a shortlisted application from the rest.
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)
The role is dual-located in San Francisco, CA and New York City, NY (both onsite 5 days/week); CBSA is reported as San Francisco since it is listed first, but the NYC metro (35620) is equally valid.
The $130,000–$500,000 salary range is unusually wide and likely reflects a broad band across multiple levels; the posting does not break it down further.
The title carries no seniority modifier and the minimum experience gate is 3 years, which maps to Mid-level, though the scope of responsibilities (owning production model performance, defining architecture) skews toward Senior. Mid was chosen as the conservative band per the tiebreaker rule.
LLM applications, retrieval systems, model evaluation, and AI-powered products are listed under the required 'Ideal Candidate Background' section but are framed with 'or' across several options, indicating breadth of acceptable backgrounds rather than all being hard gates; LLMs is retained as the representative must-have for that cluster.
All 'Preferred' items (RAG, prompt engineering, fine-tuning, AI agents, Temporal, AWS, PostgreSQL, LiteLLM, human-in-the-loop systems, monitoring/drift detection) are explicitly under a 'Preferred' heading and are marked accordingly.
Human-in-the-loop ML systems and production monitoring/drift detection are listed under 'Preferred' and are represented by the 'model monitoring' skill; no specific named tool for monitoring was given.
SOC 15-2051 (Data Scientists) is chosen as primary given the ML modeling, evaluation framework, and model lifecycle focus; 15-1252 (Software Developers) is a close runner-up given the strong production engineering and backend integration emphasis.
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.