Senior Software Engineer

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Dotdash Meredith

WebsiteLinkedInAlle Stellen

1001 - 5000 Mitarbeiter

Gegründet 2017

📱 Medien

Media • Publishing

Dotdash Meredith ist Amerikas größter digitaler und Print-Publisher, bekannt dafür, vertrauenswürdige Inhalte zu einer breiten Palette von Themen wie Gesundheit, Finanzen, Schönheit und Wohnen zu erstellen. Mit dem Engagement, die Erfahrungen zu bieten, die Menschen wünschen, und die Antworten zu liefern, die sie brauchen, erreicht Dotdash Meredith jeden Monat fast 200 Millionen Menschen durch seine verschiedenen Marken wie PEOPLE, Investopedia, Serious Eats und Byrdie. Das Unternehmen bietet innovative Werkzeuge wie das Intent Targeting Tool zur Verbesserung seiner digitalen Werbefähigkeiten. Dotdash Meredith wird für sein tiefes Fachwissen in redaktionellen Inhalten und Premium-Publishing anerkannt und ist eine vertrauenswürdige Quelle sowohl für Inspiration als auch für essenzielle Informationen.

Beschreibung

• 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

🎯 Anforderungen

• 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

🏖️ Vorteile

• 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