Senior Machine Learning Engineer
Core
Develop and deploy advanced ML solutions (classification, clustering, regression) to optimize respondent-to-survey matching and ensure data quality, while shaping MLOps practices for production reliability.
Role type
Senior Machine Learning Engineer (MLOps & Model Development)
Builds
Production ML models, inference APIs, and scalable MLOps infrastructure on AWS
Domain
Survey data optimization, cloud infrastructure, machine learning
Deliverable
production ML models | infrastructure
Required skills
Python, LightGBM, Scikit-learn, NumPy/Pandas, supervised learning, MLOps pipelines, AWS SageMaker/S3/Fargate, SQL, Snowflake, model monitoring, mentoring
Preferred skills
reinforcement learning, LLMs, Java, Docker/ECS, Terraform/CloudFormation/CDK, A/B testing frameworks, MLflow/Weights & Biases, transformer architectures/RAG
Technologies
AWS (SageMaker, S3, Fargate, Lambda, ECR), Snowflake, LightGBM, Scikit-learn, NumPy, Pandas, Docker, Terraform, MLflow, Prometheus
Responsibilities
Develop, train, and optimize ML models (classification, regression, RL, LLMs); Design and maintain ML pipelines on AWS; Own the full model lifecycle from experimentation to production deployment; Implement monitoring, logging, and alerting for deployed models; Build real-time and batch inference systems and APIs; Query large datasets using SQL and Snowflake; Mentor team members on ML best practices and MLOps; Drive technical discussions on architecture and tooling
Seniority
Senior, hands-on IC with mentorship responsibilities
