Research Engineer, Monetization AI
Meta · Bellevue, WA
$154,003–$217,000from the description
Jul 21, 2026
Bellevue, WA
Jul 23, 2026
What this job asks for AI summary
A research engineering role within Meta's ads ranking organization, focused on advancing ML systems that serve personalized advertising. Day-to-day work spans large-scale model architecture development, sequence and generative modeling, graph-aware language models, reinforcement learning, AutoML, and causal learning. The role suits researchers with deep learning or NLP backgrounds who are comfortable taking ideas from experimentation through to production systems.
Senior level · 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). 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)
No work location is specified in the posting beyond a general reference to 'country of employment.' Meta typically posts roles across multiple US offices (Menlo Park, New York, Seattle, etc.); CBSA could not be determined.
The role title is 'Research Engineer, Monetization AI' — it sits at the boundary between applied ML research (15-2051 Data Scientists) and software engineering (15-1252 Software Developers). The emphasis on novel model architectures, publications, and research-to-production pipelines tips it toward 15-2051, but the strong implementation and deployment responsibilities make 15-1252 a credible alternative.
The minimum qualifications list broad research-area experience (deep learning, RL, NLP, computer vision, recommendations, ranking, search) without specifying a single required sub-domain; all are captured as a combined must-have gate on deep learning/RL/NLP as representative hard gates.
PyTorch is listed as a required framework example ('such as PyTorch'); the qualifier signals interchangeability, so common alternatives (TensorFlow, JAX) are included.
A Master's or PhD is listed under Preferred Qualifications only, so the degree requirement is set to None.
Compensation is stated as $154,003–$217,000/year plus bonus, equity, and benefits.
Ignored 1 non-technology phrase(s) as skills (responsibilities/concepts, not named tools): generative modeling.
Read the full posting
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