Role overview

Senior Machine Learning Engineer

Requirements and responsibilities

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What You'll Do

  • 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

Technical Requirements

  • 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).

What We're Looking For

  • 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

Nice to haves

  • 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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Browse stack
FocusSenior Machine Learning EngineerRole area
Seniority signalSeniorCandidate level
StackPython, SQLPrimary skills
Location41 accepted countriesEligibility

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