Senior Software Engineer, 1

Dotdash Meredith•New York, NY

7d•$125,000 - $150,000•Remote

About The Position

People Inc. is looking for a Senior Software Engineer 1 to join our AI/ML Engineering Platform team. As part of the AI/ML Engineering Platform team, you'll be working on widely used components that help users find ways to consume content on our sites. This includes using technologies such as Vertex AI pipeline, KServe, Kafka, Elasticsearch and Vector Database to leverage the power of AI and ML use cases and build capabilities to recommend related articles, and much more! As a Senior Software Engineer 1, you will collaborate with product owners, Data Science, Platform teams, project managers, and software engineers to create service applications and contribute to the technical roadmap.

Requirements

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. Able to explain technical concepts clearly to both technical and non technical stakeholders
Solid problem solving skills with a data driven approach
Backend and API development using Python, FastAPI, Node.js, and TypeScript
Search and indexing using Elasticsearch for relevance, retrieval, and query optimization
Event driven architecture and streaming using Apache Kafka
Vector search and embeddings infrastructure using vector databases such as Milvus or Pinecone
Cloud and infrastructure using Google Cloud Platform or Amazon Web Services with containerization via Docker and orchestration through Kubernetes

Nice To Haves

Experience with KServe for model serving
Experience with Milvus or Pinecone for vector databases
Experience with Vertex AI Pipelines for ML pipelines

Responsibilities

Design and build systems, 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. Debug issues across indexing, retrieval, ranking, and serving layers
Create clear documentation for pipelines, models, APIs, and system design
Contribute to best practices for Python based ML systems, API design, and scalable infrastructure
Stay current with advancements in search, ranking, and recommendation systems. Apply them where they make practical impact