Machine Learning Engineer

About The Role

The role owns the full lifecycle of ML models - from experimental prototyping to monitored, production-grade deployment serving millions of users daily.

You will work alongside applied scientists and senior data engineers to solve problems where model accuracy, latency, and reliability all matter equally. You will ship things that are genuinely hard to build and genuinely important to the businesses depending on them.

Key Responsibilities

• Design, train, and evaluate ML models - classification, regression, ranking, NLP - for production use cases that directly impact client outcomes
• Build feature engineering pipelines in Python and PySpark, maintaining data quality standards across training and serving environments
• Own model deployment end-to-end using AWS SageMaker or Vertex AI, including versioning, A/B testing frameworks, and rollback procedures
• Monitor deployed models for data drift, feature degradation, and performance regression using automated alerting pipelines
• Write clean, testable, well-documented code; participate in code reviews and actively contribute to team ML engineering best practices
• Collaborate with data engineers on feature stores, infrastructure improvements, and pipeline reliability at scale
• Track and communicate experiments rigorously using MLflow; present model behavior and performance clearly to both technical and non-technical stakeholders

What We Are Looking For

• 1–4 years of experience in machine learning engineering or applied ML, with at least one production model deployment
• Strong Python skills and hands-on proficiency with at least one major ML framework: PyTorch, TensorFlow, or scikit-learn
• Experience with cloud ML platforms: AWS SageMaker, GCP Vertex AI, or Azure ML
• Solid understanding of ML fundamentals: model selection, regularization, evaluation metrics, bias-variance tradeoff
• Familiarity with containerization (Docker, Kubernetes) and CI/CD concepts for model deployment pipelines
• MS or BS in Computer Science, Statistics, Data Science, or a closely related field preferred
• Bonus: experience with distributed training, streaming feature pipelines (Kafka, Flink), MLflow administration, or causal modeling

Location

San Francisco Bay Area (Hybrid, 3 days/week in office)

• Seattle considered
• Remote for exceptional candidates