Staff Machine Learning Engineer at PPRO
Sao Paulo
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Jul 2, 2026
Sao Paulo
Jul 21, 2026
What this job asks for AI summary
A senior individual-contributor ML engineering role focused on payment optimization — specifically authorization rates, routing logic, and retry strategies. The position involves designing shared ML infrastructure (feature stores, model serving, experimentation frameworks), setting organization-wide MLOps standards, and mentoring senior engineers. It suits someone with deep classical ML expertise, production ML ownership experience, and familiarity with card payment systems.
Staff level · National · 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
Most people in this occupation already list these. Still required — just not what gets you shortlisted.
What the occupation pays Median $122,874 (middle half $87,544–$162,374).
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 is based in Brazil (BRL compensation, meal vouchers in BRL, SESC benefit, and pet-friendly office language consistent with PPRO's São Paulo office), so the US-based flag is set to false. No CBSA applies.
The posting is a genuine hybrid between ML/Data Science (model design, experimentation, classical ML mastery) and Software/Platform Engineering (ML infrastructure, feature stores, model serving, MLOps tooling). SOC 15-2051 (Data Scientists) was chosen because the primary framing is ML architecture and applied ML mastery; 15-1252 (Software Developers) is a strong runner-up given the heavy platform-engineering and production-systems emphasis.
The title is explicitly 'Staff Machine Learning Engineer' and the scope — setting org-wide ML direction, mentoring senior engineers, cross-team architectural authority — clearly supports the Staff band.
All skills listed are drawn from the 'What We Are Looking For' section, which functions as the requirements block. XGBoost and LightGBM are listed together as interchangeable exemplars of classical ML expertise and are combined into one skill entry with LightGBM as an alternative.
No total years of experience figure is stated anywhere in the posting.
Compensation is quoted only in BRL (meal vouchers at BRL 54/day, learning budget at BRL 3,000/year) but no salary range is provided, so the pay band fields are left null/unspecified.
The hybrid working policy requires 3 days/week on-site, so remote is set to false.
This role's work location reads as outside the US — the candidate pool, comp, and contention benchmarks here are US-only (BLS employment, Adzuna/USAJOBS demand, and certified H-1B wages), so treat them as a rough US reference, not a local market.
Read the full posting
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