JD - Senior Software Engineer 1, ML

Dotdash Meredith•New York, NY

4d•$160,000 - $195,000•Hybrid

About The Position

As a Senior Software Engineer for personalization, you will own the design, development, and continuous improvement of the recommendation algorithm that powers the user's personalized product feed. You'll work with a rich dataset of user-saved products and a live ingestion pipeline pulling from thousands of retailer feeds to build a system that learns each user's unique preferences across brand, category, color, price point, and fit. This is a high-ownership, high-impact role. You will collaborate closely with product, engineering, and data teams to define what great personalization looks like — and then build it. Remote or Hybrid 3x a month In-office Expectations: This position offers remote work flexibility; however, if you reside within a commutable distance to one of our offices in New York, Des Moines, Birmingham, Los Angeles, Chicago, or Seattle, the expectation is to work from the office two days per week.

Requirements

Bachelor’s degree in Computer Science, Engineering, or a related field.
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 (e.g., Milvus, Pinecone) for product or content similarity at catalog scale.
Production experience serving real-time, low-latency ML predictions and managing the full model lifecycle — training, deployment, versioning, and monitoring — on cloud ML platforms such as AWS SageMaker or GCP Vertex AI (including Vertex AI Pipelines).
Rigorous experimentation discipline: experiment design, A/B and multivariate testing, and the analytical ability to translate model results into clear product and business decisions.
Extensive backend engineering with strong 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, transformation, and feature engineering.
Applied NLP and/or computer vision experience extracting structured attributes (category, color, material, fit) from unstructured product descriptions and imagery.
Strong API and infrastructure foundations: REST and GraphQL design with secure auth (OAuth/JWT), Git-based workflows, containerization with Docker and Kubernetes, and production observability with Grafana, Kibana, and APM tooling.
Curiosity and pragmatism around emerging AI, particularly LLMs and modern retrieval/ranking techniques, with a track record of bringing new approaches into real production use cases.
Strong written and verbal communication, able to explain technical tradeoffs to both technical and non-technical stakeholders, with a data-driven approach to problem solving.

Responsibilities

Design and build the core personalization engine using user-saved product data as behavioral signals.
Develop multi-signal recommendation models that incorporate brand affinity, product category, color palette, fit/sizing signals, price sensitivity, and trends.
Implement and evaluate a range of approaches including collaborative filtering, content-based filtering, and hybrid neural architectures.
Build and maintain product embedding models that capture rich semantic similarity across the retailer feed catalog.
Develop cold-start strategies to generate high-quality recommendations for new users with limited save history.
Design and maintain robust pipelines to ingest, normalize, and enrich product feeds from thousands of retail partners.
Collaborate on a unified product taxonomy and attribute extraction layer that standardizes inconsistent retailer data into coherent features (category, color, material, fit, etc.).
Leverage NLP and computer vision techniques to extract attributes from unstructured product descriptions and images.
Partner with the data engineering team to maintain data quality, freshness, and catalog coverage at scale.
Build and own the ranking and re-ranking layer that assembles each user's personalized feed in real time.
Develop and tune multi-objective ranking that balances relevance, novelty, diversity, and business goals (e.g., promoted/sponsored retailer partnerships).
Implement feedback loops that continuously update user preference models based on implicit signals (saves, clicks, dwell time, shares).
Build A/B testing solutions to rigorously evaluate ranking and recommendation changes against key engagement metrics.
Own production systems.
Debug issues across indexing, retrieval, ranking, and serving layers
Create clear documentation for pipelines, models, APIs, and system design.
Contribute to best practices for ML systems, API design, and scalable infrastructure.
Stay current with advancements in recommendation, ranking, and personalization systems and apply them where they make practical impact.