Machine Learning Engineer II

About The Role :

Condé Nast is seeking a motivated and skilled Machine Learning Engineer I to support the productionization of machine learning projects in Databricks or AWS environments for the Data Science team.

This role is ideal for an engineer with a strong foundation in software development, data engineering, and machine learning, who enjoys transforming data science prototypes into scalable, reliable production pipelines.

Note: This role focuses on deploying, optimizing, and operating ML models rather than building or researching new machine learning models.

Primary Responsibilities

• Design, build, and operate scalable, highly available ML systems for batch and real-time inference.

• Own the end-to-end production lifecycle of ML services, including deployment, monitoring, incident response, and performance optimization.

• Build and maintain AWS-native ML architectures using services such as EKS, SageMaker, API Gateway, Lambda, and DynamoDB.

• Develop and deploy low-latency ML inference services using FastAPI/Flask or gRPC, running on Kubernetes (EKS).

• Design autoscaling strategies (HPA/Karpenter), rollout mechanisms, traffic routing, and resource tuning for ML workloads.

• Engineer near-real-time data and inference pipelines processing large volumes of events and requests.

• Collaborate closely with Data Scientists to translate prototypes into robust, production-ready systems.

• Implement and maintain CI/CD pipelines for ML services and workflows using GitHub Actions and Infrastructure-as-Code.

• Improve observability, logging, alerting, and SLA/SLO adherence for critical ML systems.

• Follow agile engineering practices with a strong focus on code quality, testing, and incremental delivery.
   

Desired Skills & Qualifications

• 4-7+ years of experience in Machine Learning Engineering, MLOps, or Backend Engineering.

• Strong foundation in system design, distributed systems, and API-based service architectures.

• Proven experience deploying and operating production-grade ML systems on AWS.

• Strong proficiency in Python, with experience integrating ML frameworks such as PyTorch, TensorFlow, scikit-learn, and working with data processing libraries like Pandas, NumPy, and PySpark.

• Solid experience with AWS services, including (but not limited to): EC2, S3, API Gateway, Lambda, IAM, VPC Networking, DynamoDB.

• Hands-on experience building and operating containerized microservices using Docker and Kubernetes (preferably EKS).

• Experience building and deploying ML inference services, using:

• FastAPI / Flask / gRPC

• TorchServe, TensorFlow Serving, Triton, vLLM, or custom inference services

• Strong understanding of data structures, data modeling, and software architecture.

• Experience designing and managing CI/CD pipelines and Infrastructure-as-Code (Terraform) for ML systems.

• Strong debugging, performance optimization, and production troubleshooting skills.

• Excellent communication skills and ability to collaborate effectively across teams.

• Outstanding analytical and problem-solving skills.

• Undergraduate or Postgraduate degree in Computer Science or a related discipline.

Preferred Qualifications

• Experience with workflow orchestration and ML lifecycle tools such as Airflow, Astronomer, MLflow, or Kubeflow.

• Experience working with Databricks, Amazon SageMaker, or Spark-based ML pipelines in production environments.

• Familiarity with ML observability, monitoring, or feature management (e.g., model performance tracking, drift detection, feature stores).

• Experience designing or integrating vector search, embedding-based retrieval, or RAG-style systems in production is a plus.

• Prior experience operating low-latency or high-throughput services in a production environment.