Senior Machine Learning 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 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.