GOAT Group

Senior Machine Learning Engineer

Vaga remota de Senior Machine Learning Engineer com fit claro de localização do candidato.

Publicada19 de jul. de 2026

Países elegíveis41 países aceitos

Sinal de senioridadeSenior

Modelo de trabalhoRemoto

Locais aceitos para candidatos

AlbâniaArmêniaÁustriaBielorrússiaBélgicaBulgária+35 mais

Resumo da vaga

Conteúdo da vaga extraído em seções para revisão mais rápida.

Own the full lifecycle of predictive models in production — architecture, training pipelines, inference infrastructure, deployment, and ongoing model health
Build and operate the systems that route model outputs into live product surfaces: search ranking, recommendations, feed ordering, and related user-facing experiences
Establish and maintain model monitoring, alerting, drift detection, and retraining cadences — the feedback loops that keep deployed models accurate over time
Partner closely with Data Science, Data Engineering, Product Management, and backend engineering to move work from validated approach to production system
Own the decision-making process on whether to leverage ML infrastructure & expertise from our parent company, GOAT Group, and when to advocate for building in-house solutions.
Contribute to ML infrastructure decisions — serving architecture, feature computation, pipeline orchestration — with an eye toward what scales as the team and model count grows
Set technical standards and raise the bar for how ML systems are built, evaluated, and operated across the pod

7+ years of engineering experience, with substantial depth in production machine learning systems.
Demonstrated end-to-end ownership: training pipelines through deployed inference, not just modeling.
Advanced knowledge of ML, AI and statistical models, as well their application in e-commerce settings.
Strong proficiency in Python; SQL; DBT; airflow or similar.
Solid software engineering fundamentals.
Experience with ranking, retrieval, or recommendation systems.
Demonstrated expertise with ML lifecycle tooling — experiment tracking, model versioning, pipeline orchestration, drift detection — and comfort working with modern data infrastructure (cloud warehouse, search/retrieval systems).

Takes ownership of developing repeatable end-to-end processes, not just outcomes
Evaluates technical approaches against production constraints — latency, reliability, retraining cost — not just offline metrics
Brings judgment to architecture decisions: knows when to reach for a complex approach and when a simpler one is the right call
Treats model health as a permanent responsibility, not a launch milestone
Communicates clearly with non-technical partners — can translate model behavior, tradeoffs, and timelines into terms that product and business stakeholders can act on
A willing collaborator who keeps people informed and works through ambiguity without going quiet
Genuine curiosity about the domain — fashion, resale, taste — and the specific ML problems it creates

Experience with semantic enrichment, NLP, or multi-modal ML in a production context
Genuine curiosity about the domain — fashion, resale, style — and the specific ML problems it creates

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