Machine Learning Engineer

Responsibilities

Design and implement end-to-end machine learning pipelines spanning data ingestion, feature engineering, model training, offline evaluation, and production serving Develop and optimize models for large-scale applications such as ranking, recommendations, classification, and natural language understanding Instrument model performance with robust monitoring, alerting, and dashboards to detect regressions and maintain production reliability Drive A/B experiment design and analysis to validate model improvements, articulate trade-offs, and make data-informed launch decisions Architect scalable ML infrastructure and reusable components that accelerate development velocity across the team Identify and resolve complex model quality issues through systematic debugging, root cause analysis, and defensive engineering practices Collaborate with product managers, data scientists, and software engineers to translate product requirements into ML problem formulations and technical roadmaps Define and evolve ML platform standards, coding best practices, and testing frameworks to improve engineering quality across the organization Mentor other engineers on ML methodology, system design, and engineering craft, and contribute to recruiting by conducting technical interviews

Qualifications

6+ years of experience developing and deploying machine learning models in production environments Experience with large-scale ML frameworks such as PyTorch, TensorFlow, or JAX, and proficiency in Python for model development and data processing Experience designing and optimizing ML systems for ranking, recommendations, classification, or natural language understanding at scale Experience with feature engineering, offline and online model evaluation, and A/B experimentation methodologies Experience building reliable, monitored ML pipelines with observability tooling including dashboards, alerting, and logging Familiarity with ML platform infrastructure including feature stores, model registries, and automated retraining pipelines Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews) Experience with distributed training frameworks and large-scale model optimization techniques such as quantization, distillation, or pruning Track record of driving cross-functional ML projects from problem definition through production impact, including influencing technical direction across teams Demonstrated ongoing AI skill development (e.g., prompt/context engineering, agent orchestration) and staying current with emerging AI technologies Demonstrated ability to integrate AI tools to optimize/redesign workflows and drive measurable impact (e.g., efficiency gains, quality improvements) Experience applying ML to integrity, personalization, or real-time inference systems with strict latency requirements