Senior Applied AI/ML Scientist - Retailer Growth at Faire
San Francisco, CA
$196,000–$269,500from the description
Jul 17, 2026
San Francisco, CA
Jul 21, 2026
What this job asks for AI summary
An applied machine learning role on a retailer growth team, focused on building AI/ML systems for paid marketing and top-of-funnel acquisition. Day-to-day work spans bidding optimization, audience targeting, causal inference for experimentation, and using large language models to generate programmatic content for answer engine optimization. Suits ML practitioners with e-commerce experience who can independently design and ship end-to-end solutions.
Mid level · 3+ 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 260 people in the San Francisco-Oakland-Berkeley, CA area plausibly meet what this posting asks for (data scientists). range 190–330
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 (8)
The role title is 'Applied AI/ML Scientist' with no explicit seniority level in the title; advertised seniority is Unspecified.
The 3+ years requirement and scope (executing within a team, working cross-functionally without a management mandate) support a Mid-level classification despite the relatively high compensation range.
A Master's or PhD in CS, Statistics, or a related STEM field is listed under 'Great to Haves' as 'Highly recommended' — not a hard gate — so the degree requirement is set to None.
The technical methods listed in Qualifications ('LTV modeling, NLP, LLMs, causal ML, bidding optimization') are treated as hard gates because they appear in the required Qualifications section, even though no specific programming language or framework is named.
Paid marketing experience, SEO/AEO optimization, and LLM/programmatic content generation experience are listed under 'Great to Haves' and are therefore preferred.
The role is hybrid (3 days/week in-office in San Francisco); it is not fully remote.
SOC confidence is Medium: the role blends applied ML engineering and data science; 15-2051 (Data Scientists) is the best fit given the emphasis on ML modeling, causal inference, and statistical methods, but 15-1299 is a reasonable runner-up given the strong engineering/systems-building component.
Ignored 2 non-technology phrase(s) as skills (responsibilities/concepts, not named tools): LTV modeling, bidding optimization.
Read the full posting
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