Назад
4 дня назад

Senior Machine Learning Engineer Embedded Insights

229 000 - 315 000$
Формат работы
hybrid
Тип работы
fulltime
Грейд
senior
Страна
US
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Описание вакансии

TL;DR
Senior Machine Learning Engineer Embedded Insights (Machine Learning/Python/SQL): Building and deploying machine learning-powered features for a consumer-facing product with an accent on experimentation, model productionization, and feedback loops. Focus on translating business needs into ML problems, defining success metrics and guardrails, and maintaining model health through monitoring, retraining, alerts, and dashboards.

Senior Machine Learning Engineer Embedded Insights

Company

Plaid Inc.

Conditions

1 day agoSalary: 229K - 315K

Skills

Candidate Availability

Required and preferred rules are kept separate and reflect the wording in the original posting.

About the Role

You will build machine learning features for a consumer-facing product, translate business needs into machine learning problems, iterate toward product-market fit, and define success metrics. You will validate opportunities, productionize models, develop feedback loops, optimize and monitor models, and communicate technical decisions clearly.

Requirements

  • 6+ years of machine learning experience.
  • Experience deploying models into customer-facing systems.
  • Experience identifying, defining, and proposing machine learning opportunities.
  • Ability to analyze large and complex financial datasets.
  • Experience taking machine learning systems from experimentation through production and ongoing improvement.
  • Proficiency in SQL, Python, data visualization, and analysis tools.
  • Ability to communicate complex technical systems and decisions to cross-functional partners.

Responsibilities

  • Build machine learning-based features for a consumer-facing product.
  • Translate business requirements into machine learning problems and influence product strategy.
  • Iterate and experiment to drive product-market fit.
  • Define success metrics and guardrails for machine learning features.
  • Build data feedback loops with machine learning engineers.
  • Analyze datasets and complete proofs of concept for machine learning opportunities.
  • Productionize and deploy models in customer-facing products.
  • Develop features, retraining cadences, metrics, alerts, and dashboards to maintain model health.
  • Communicate technical decisions, tradeoffs, and system behavior to partners.

Benefits

  • Equity
  • Medical insurance
  • Dental insurance
  • Vision insurance
  • 401(k)

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