machine learning engineer for customer engagement

HireHi; Lisboa; Presencial; EUR 75 000 - 110 000; Tempo integral

Vantagens oferecidas por esta oferta de emprego

Private Medical Insurance

Life/Risk Assurance

Meal Allowance: 8.55€ per day

Paid Annual Leave (22 days)

Global Lifestyle Reimbursement Account

Paid Sabbatical

Complete laptop workstation

Community Days

Resumo da oferta

HireHi ищет инженера ML для разработки и развёртывания ядра ML-систем в продуктовом стекe. Вы будете строить модели для оптимизации отправки сообщений, рекомендации и персонализации кампаний, а также отвечать за весь цикл модели от анализа данных до продакшн.

Требуется опыт 5+ лет вывода моделей в продакшн, владение Databricks, Spark, Python/Scala, контейнеризации (Kubernetes) и микросервисной архитектурой.

Qualificações

• 5+ лет hands-on MLE в продакшне на больших данных
• Опыт построения и оптимизации конвейеров данных и моделей
• Опыт работы с микросервисами и контейнерами (Kubernetes)
• Знание Python или Scala, читаемость кода
• Опыт работы с Databricks/Spark, AWS, Ray

Responsabilidades

• Разработка и развёртывание core ML возможностей продукта (оптимизация отправки, рекомендации, персонализация)
• Управление жизненным циклом модели: от EDA до продакшн и мониторинг
• Построение масштабируемых data pipelines и архитектуры сервинга
• Сотрудничество с Backend и Platform командами, установка стандартов
• Наставничество инженеров и направление ML-стратегии
• Интеграция моделей в основной продукт и улучшение latency

Conhecimentos

MLE-продакшн

Databricks/Spark

Python/Scala

Микросервисы

Kubernetes

Ferramentas

Databricks

Spark

AWS

Ray

Kubernetes

Descrição da oferta de emprego

Описание:

Iterable is an AI customer engagement platform for enterprise organizations. It helps brands activate customer data, create cross-channel experiences, and optimize engagement using its Nova Intelligence AI layer, with enterprise-grade security and compliance.

Задачи:

• Build and ship core product ML capabilities, including send-time and frequency optimization, recommendation engines, and campaign personalization
• Own the full model lifecycle, from EDA and feature engineering through distributed training, production deployment, latency monitoring, and online experimentation
• Build and scale data pipelines and serving architectures using Databricks, Spark, AWS, Ray, and Kubernetes
• Partner with Backend and Platform teams to improve feature serving and data architecture and establish engineering standards
• Mentor engineers and help set the technical direction for ML
• Build, evaluate, and integrate models that add intelligence to the core product
• Enhance models that optimize the send time and frequency of outgoing message campaigns
• Design data pipelines that simplify implementing and testing new models
• Work with platform engineers to enhance feature- and model-serving architecture
• Benchmark and improve the multi-armed bandit implementation
• Build tools to visualize and debug features and models

Требования:

• 5+ Years of hands‑on MLE experience putting complex models into production at scale
• Strong Databricks and Spark expertise, including building, optimizing, and scaling production data pipelines
• Proven experience taking deep learning and ensemble architectures from training through optimization to production deployment
• Strong Python and/or Scala skills, with an emphasis on readable, modular, and maintainable systems code
• Hands‑on experience with microservice backends, distributed compute (Ray/Spark), and containers (Kubernetes)
• Prioritize serving latency, reliability, and measurable customer impact over novel theoretical architectures
• Будет плюсом: large-scale recommendation systems, feature stores, model observability tools, Infrastructure-as-Code (Terraform, Pulumi)

Условия:

• Competitive salaries and meaningful equity
• Private Medical Insurance
• Life/Risk Assurance
• Meal Allowance: 8.55€ per day
• Community Days
• Paid Annual Leave (22 days)
• Global Lifestyle Reimbursement Account
• Paid Sabbatical
• Complete laptop workstation