Senior Software Engineer

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Dotdash Meredith

SiteLinkedInTodas as Vagas

1001 - 5000 funcionários

Fundada em 2017

📱 Mídia

Media • Publishing

A Dotdash Meredith é a maior editora digital e impressa dos Estados Unidos, reconhecida por criar conteúdos confiáveis sobre uma ampla gama de tópicos, incluindo saúde, finanças, beleza e lar. Com o compromisso de fornecer as experiências que as pessoas desejam e as respostas de que precisam, a Dotdash Meredith alcança quase 200 milhões de pessoas a cada mês através de suas diversas marcas, como PEOPLE, Investopedia, Serious Eats e Byrdie. A empresa oferece ferramentas inovadoras, como a Ferramenta de Segmentação por Intenção, para aprimorar suas capacidades de publicidade digital. A Dotdash Meredith é reconhecida por sua profunda expertise em conteúdo editorial e publicação premium, tornando-se uma fonte confiável tanto para inspiração quanto para informações essenciais.

Descrição

• Design and build scalable distributed systems, backend platforms, compatible with AI/ML infrastructure for search, retrieval, ranking, recommendation, and personalization use cases • Manage scalable ML pipelines using Vertex AI Pipelines for training, evaluation and deployment to support ranking, retrieval, and recommendation personalization use cases • Develop and maintain data pipelines that support feature generation, model training, and analytics workflows • Own vector generation via Milvus, storage, and retrieval workflows • Implement model serving solutions using KServe and build APIs using FastAPI for low latency inference • Build observability and monitoring for models and pipelines • Track performance, drift, failures, and data quality issues • Collaborate with data scientists, product managers, and platform teams to define and deliver ML driven features • Investigate production issues across data pipelines, models, and services • Identify bottlenecks and improve reliability and performance • Create and maintain clear documentation for pipelines, models, APIs, and operational processes • Develop internal tools and dashboards to provide visibility into data processing and model behavior for stakeholders • Contribute to engineering standards, code quality, and best practices across Python-based services and ML systems • Stay current with ML infrastructure, MLOps practices, and relevant tools • Bring in improvements where they add clear value • Collaborate with product, data science, and frontend teams to deliver high quality search and feed experiences • Own production systems and debug issues across indexing, retrieval, ranking, and serving layers • Create clear documentation for pipelines, models, APIs, and system design

🎯 Requisitos

• Bachelor’s degree in Computer Science, Engineering, or a related field • 6+ years of experience building scalable backend systems and services • 5+ years of experience developing software using object oriented languages, with strong proficiency in Python, Node.js, and TypeScript • Hands on experience with ES for search, indexing, and relevance tuning • Experience with event driven systems using Apache Kafka for real time data pipelines and processing • Strong understanding of version control systems including Git and platforms like Bitbucket • Experience with observability and monitoring tools such as Grafana, Kibana, and APM • Familiarity with cloud platforms including AWS and GCP, along with containerization using Docker and orchestration with Kubernetes • Comfortable deploying, versioning, and monitoring models in production • Curiosity to learn new technologies, especially in AI, LLMs, and modern search and recommendation systems, with a focus on applying them in real production use cases • Experience designing and building data pipelines using Apache Beam and Apache Airflow for ingestion, transformation, and feature pipelines • Familiarity with experimentation and analytics tools such as Jupyter Notebook and Apache Spark to track and reproduce experiments • Strong experience designing and consuming RESTful and GraphQL APIs, including versioning, documentation, and security practices like OAuth and JWT • Good understanding of machine learning concepts including supervised learning, unsupervised learning, deep learning, and natural language processing, with practical application in ranking, retrieval, and personalization • Beginner level experience managing ML pipelines using Vertex AI Pipelines for training, evaluation, and deployment workflows • Ability to review code, provide clear feedback, and improve overall engineering quality • Strong communication skills

🏖️ Benefícios

• medical, dental, vision, prescription drug coverage • unlimited paid time off (PTO) • adoption or surrogate assistance • donation matching • tuition reimbursement • basic life insurance • basic accidental death & dismemberment • supplemental life insurance • supplemental accident insurance • commuter benefits • short term and long term disability • health savings and flexible spending accounts • family care benefits • a generous 401K savings plan with a company match program • 10-12 paid holidays annually • generous paid parental leave (birthing and non-birthing parents) • voluntary benefits such as pet insurance • accident, critical and hospital indemnity health insurance coverage • life and disability insurance