About this role
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.