Senior Machine Learning Engineer at Teampathy
—
Jul 25, 2026
—
Jul 26, 2026
What this job asks for AI summary
A senior engineering role focused on building and maintaining personalization and recommendation systems across the full ML lifecycle — from data preprocessing and feature engineering through to deployment and monitoring. The work centers on production ML models, with particular emphasis on Graph Neural Networks and hybrid systems that combine traditional ML with large language models. Suited to experienced ML engineers with a background in recommendation or personalization domains.
Senior level · 5+ years · Remote
“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 5,000 people nationally plausibly meet what this posting asks for (data scientists). range 2,100–7,500
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 $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 (6)
The role is posted by a staffing/consulting firm (Teampathy) connecting developers with global clients; the actual work location and remote arrangement are not explicitly stated, but the global/distributed framing strongly implies remote work.
SOC classification is Medium confidence: the role blends ML research/modeling (15-2051 Data Scientists) with substantial production engineering and deployment responsibilities (15-1252 Software Developers); the emphasis on end-to-end ML lifecycle and production deployment makes it a genuine hybrid.
TensorFlow is listed as the primary named library alongside PyTorch and scikit-learn under a single 'such as' requirement — all three are captured under one skill with alternatives per the interchangeable-options rule.
ML model deployment is extracted as a distinct required skill because the JD explicitly gates on 'hands-on experience building and deploying machine learning models in a production environment', even though it names no specific deployment tool.
A Master's or PhD is listed under 'Nice to Have', so the degree requirement is set to None — it is preferred, not a hard gate.
GNNs and LLMs appear in both the responsibilities narrative and the 'Nice to Have' section; they are marked preferred, with the 'Nice to Have' section being the authoritative gate signal.
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.