JD - Senior Software Engineer 1, ML

Meredith Corporation

Job Overview

Location

Remote US

Job Type

Full-time

Full Job Description

📋 Description

• Design and build the core personalization engine using user-saved product data as behavioral signals to power a personalized product feed for millions of users.
• Develop multi-signal recommendation models incorporating brand affinity, product category, color palette, fit/sizing signals, price sensitivity, and trending patterns.
• Implement and evaluate recommendation approaches including collaborative filtering, content-based filtering, and hybrid neural architectures to optimize user engagement.
• Build and maintain product embedding models that capture semantic similarity across a catalog of products from thousands of retail partners.
• Design cold-start strategies to deliver high-quality recommendations for new users with limited save history or interaction data.
• Design and maintain robust data ingestion pipelines to consume, normalize, and enrich live product feeds from thousands of retail sources.
• Collaborate on a unified product taxonomy and attribute extraction layer to standardize inconsistent retailer data into coherent features such as category, color, material, and fit.
• Apply NLP and computer vision techniques to extract structured attributes from unstructured product descriptions and images.
• Partner with data engineering to ensure data quality, freshness, and catalog coverage at scale across global retail feeds.
• Build and own the real-time ranking and re-ranking layer that assembles personalized feeds for each user, balancing relevance, novelty, diversity, and business goals like promoted partnerships.
• Implement feedback loops that continuously update user preference models using implicit signals such as saves, clicks, dwell time, and shares.
• Design and execute A/B and multivariate tests to rigorously evaluate recommendation and ranking changes against key engagement metrics.
• Own production systems across indexing, retrieval, ranking, and serving layers, debugging latency, accuracy, and scalability issues.
• Create and maintain clear documentation for ML pipelines, models, APIs, and system architecture.
• Contribute to best practices for ML systems, API design, and scalable infrastructure to ensure reliability and performance.
• Stay current with advancements in recommendation systems, ranking algorithms, and personalization techniques, applying practical improvements to production systems.
• Develop and deploy embedding models and vector retrieval systems using vector databases such as Milvus or Pinecone at catalog scale.
• Serve real-time, low-latency ML predictions using cloud ML platforms like AWS SageMaker or GCP Vertex AI, managing full lifecycle: training, deployment, versioning, and monitoring.
• Build and maintain event-driven architectures using Apache Kafka for streaming ingestion and feature engineering.
• Utilize Python, PyTorch, TensorFlow, or JAX for model development, and Node.js and TypeScript for backend services and APIs.
• Design REST and GraphQL APIs with secure authentication via OAuth/JWT, and ensure infrastructure reliability through Docker, Kubernetes, and observability tools like Grafana, Kibana, and APM.
• Apply Elasticsearch for search relevance, query optimization, and retrieval tasks within the recommendation system.
• Communicate technical tradeoffs clearly to both engineering and non-technical stakeholders using a data-driven approach to decision-making.
• Demonstrate curiosity and pragmatism around emerging AI techniques, particularly LLMs and modern retrieval/ranking methods, with a track record of integrating them into production systems.

🎯 Requirements

• 5+ years of ML engineering experience focused on recommendation systems, personalization, or search ranking with hands-on depth in collaborative filtering, matrix factorization, content-based, and hybrid neural approaches.
• Proven experience designing, training, and deploying embedding models and vector retrieval systems (e.g., Milvus, Pinecone) at catalog scale.
• Production experience serving real-time, low-latency ML predictions and managing the full model lifecycle on cloud ML platforms such as AWS SageMaker or GCP Vertex AI.
• Extensive backend engineering proficiency in Python and modern ML frameworks (PyTorch, TensorFlow, or JAX), plus working knowledge of Node.js and TypeScript.
• Experience designing large-scale data and feature pipelines using Apache Kafka, Spark, Beam, Airflow, or Flink for streaming ingestion and transformation.
• Applied NLP and/or computer vision experience extracting structured attributes (category, color, material, fit) from unstructured product descriptions and imagery.

🏖️ Benefits

• Annual bonuses and short- and long-term incentives
• Unlimited paid time off (PTO)
• Medical, dental, vision, and prescription drug coverage
• Generous 401K savings plan with company match
• 10-12 paid holidays annually
• Generous paid parental leave for birthing and non-birthing parents
• Tuition reimbursement
• Adoption or surrogate assistance
• Donation matching
• Health savings and flexible spending accounts
• Family care benefits
• Voluntary benefits including pet insurance, accident, critical illness, and hospital indemnity coverage
• Basic and supplemental life and disability insurance
• Commuter benefits

Skills & Technologies

Python

JavaScript

TypeScript

Node.js

FastAPI

Senior

Remote

$170k-195k

Degree Required

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Meredith Corporation

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About Meredith Corporation

Meredith Corporation is a diversified media company that operates magazine brands such as People, Better Homes & Gardens, InStyle, and Southern Living, local television stations reaching nearly 11 percent of U.S. households, and digital and marketing services. The company creates and distributes content across print, broadcast, online, mobile, tablet, and video platforms, while offering integrated marketing, advertising, and data solutions for national and local clients.

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