Senior ML Engineer

We're looking for a **Senior Machine Learning Engineer** to join our small, growing AI/ML team. You'll develop and deploy advanced ML solutions (classification, clustering, and regression) to optimize how we match respondents to surveys and ensure data quality. Beyond model development, you'll play a key role in shaping the MLOps practices that keep those models reliable in production: pipelines, deployment, monitoring, and scaling on AWS. This role demands autonomy, deep technical expertise, and a strong ability to mentor others. You'll collaborate with data engineers, software engineers, and product teams while helping design, develop, and scale our ML infrastructure. **Responsibilities** * Develop, train, and optimize machine learning models, primarily classification and regression using gradient-boosted frameworks (LightGBM), with the opportunity to expand into advanced ML architectures, including reinforcement learning and LLMs, as our capabilities grow. * Design, build, and maintain ML pipelines on AWS (SageMaker, S3, Fargate). * Own the full model lifecycle, from experimentation and training through production deployment and ongoing monitoring. * Implement monitoring, logging, and alerting for deployed models to ensure continued effectiveness. * Build and integrate real-time and batch inference systems, APIs, and services for model serving. * Work with Snowflake and cloud data infrastructure to access, prepare, and query large datasets using SQL. * Collaborate with engineering, product, and business stakeholders to deliver data-driven solutions. * Mentor team members, providing guidance on best practices in ML development and MLOps. * Drive technical discussions on model architecture, tooling choices, and team engineering standards. **Required Skills** * 5+ years of experience building, deploying, and scaling ML models in production. * Proficient in Python with strong experience in ML libraries (LightGBM, Scikit-learn, NumPy/Pandas). * Deep understanding of supervised learning; familiarity with reinforcement learning and LLMs is a plus. * Demonstrated ability to diagnose production model issues, design experiments to validate improvements, and make data-informed tradeoff decisions. * Strong MLOps expertise: pipelines, CI/CD for ML, model deployment, and monitoring. * Hands-on experience with AWS services for ML (SageMaker, S3, Fargate, Lambda, ECR). * SQL proficiency with experience querying large datasets (Snowflake or similar). * Ability to work autonomously and make key technical decisions related to architecture, standards, and tooling. * Experience mentoring others and teaching best practices in ML development and MLOps. **Nice to Have** * Java experience for integration with our application layer. * Understanding of containerization and orchestration (Docker, ECS). * Experience with infrastructure as code (Terraform, CloudFormation, CDK). * Experience building A/B testing or experimentation frameworks. * Experience with experiment tracking tools (MLflow, Weights & Biases). * Background in data engineering or building data pipelines. * Experience with model monitoring or observability tools (CloudWatch, Evidently AI, Prometheus). * Familiarity with transformer architectures, fine-tuning, or RAG patterns. **Why Join Us?** * Ownership & Autonomy: Small team where you'll have the freedom to shape our ML stack, and your contributions have direct, visible impact. * Variety of Work: No siloed roles. You'll work across the full ML lifecycle: data engineering, modeling, infrastructure, and applications. * Cutting-Edge Tech: Build scalable MLOps infrastructure, with a roadmap toward RL and LLM capabilities. * Collaborative Culture: A small, tight-knit ML team where you'll pair on architecture decisions, share code reviews, and have a direct line to stakeholders. * Impact-Driven: Your models will serve millions of survey respondents, directly shaping how data-driven decisions are made at scale.