Senior Software Engineer

Dotdash Meredith

. Design and build scalable distributed systems and backend platforms compatible with AI/ML infrastructure .

Posted 9/16/2026full-timeNew York City • California • United StatesSenior💰 $125,000 - $150,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in designing and building scalable distributed systems and backend platforms, with a strong focus on AI/ML infrastructure. Proficient in developing data pipelines, managing model training and deployment, and ensuring system reliability and performance.

Highest-signal resume keywords

Python ProgrammingNode.js DevelopmentElasticsearch ExperienceApache Kafka ProficiencyModel Serving with KServe

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Backend System DevelopmentData Pipeline DesignRESTful API DesignGraphQL API ConsumptionMachine Learning FundamentalsObject Oriented ProgrammingVersion Control with GitMonitoring with GrafanaContainerization with DockerOrchestration with Kubernetes

Soft Skills

Strong Communication SkillsProblem-Solving Skills

Tools & Technologies

Vertex AI PipelinesApache BeamApache AirflowJupyter NotebookApache Spark

Industry Keywords

AI/ML InfrastructureMLOpsData QualityPerformance TrackingObservability

Tech Stack

Tools & technologies

AirflowApacheAWSDistributed SystemsDockerElasticSearchGoogle Cloud PlatformGrafanaGraphQLJavaScriptKafkaKubernetesNode.jsPythonSparkTypeScript

About the role

Key responsibilities & impact

Design and build scalable distributed systems and backend platforms compatible with AI/ML infrastructure
Manage Vertex AI Pipelines for model training, evaluation, and deployment
Develop and maintain data pipelines for feature generation, model training, and analytics workflows
Own vector generation, storage, and retrieval workflows using Milvus
Implement model serving solutions using KServe
Build low-latency inference APIs with FastAPI
Build observability and monitoring for models and pipelines
Track performance, drift, failures, and data quality issues
Collaborate with data scientists, product managers, platform teams, frontend teams, project managers, and software engineers
Investigate production issues across data pipelines, models, and services
Identify bottlenecks and improve reliability and performance
Document pipelines, models, APIs, system designs, and operational processes
Develop internal tools and dashboards for data processing and model behavior visibility
Contribute to engineering standards, code quality, and best practices
Own production systems and debug indexing, retrieval, ranking, and serving layers
Deliver ML-driven search, feed, ranking, retrieval, recommendation, and personalization experiences
Stay current with ML infrastructure, MLOps, search, ranking, and recommendation advancements

Requirements

What you’ll need

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
Strong proficiency in Python, Node.js, and TypeScript
Hands-on experience with Elasticsearch for search, indexing, and relevance tuning
Experience with Apache Kafka event-driven systems and real-time data pipelines
Strong understanding of Git and platforms like Bitbucket
Experience with Grafana, Kibana, and APM
Familiarity with AWS and GCP
Experience with Docker and Kubernetes
Comfortable deploying, versioning, and monitoring models in production
Experience designing and building data pipelines using Apache Beam and Apache Airflow
Familiarity with Jupyter Notebook and Apache Spark
Strong experience designing and consuming RESTful and GraphQL APIs
Knowledge of OAuth and JWT security practices
Understanding of supervised learning, unsupervised learning, deep learning, and natural language processing
Beginner-level experience managing ML pipelines using Vertex AI Pipelines
Ability to review code and provide clear feedback
Strong communication skills
Solid problem-solving skills with a data-driven approach
Ability to work hybrid in NYC, Des Moines, LA, Seattle, or Chicago
E-Verify employment authorization verification applies to newly hired employees

Benefits

Comp & perks

Annual bonuses
Short- and long-term incentives
Medical coverage
Dental coverage
Vision coverage
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 accounts
Flexible spending accounts
Family care benefits
Generous 401K savings plan with a company match program
10-12 paid holidays annually
Generous paid parental leave (birthing and non-birthing parents)
Pet insurance
Accident, critical and hospital indemnity health insurance coverage
Life and disability insurance
Hybrid work arrangement with remote work up to 2 days per week