Machine Learning Engineer

This is a fully remote position, open to applicants in California.

📋 Description

• Take ownership of small to medium components of machine learning systems from the technical design phase through implementation and delivery.

• Convert technical requirements into maintainable code and deliver workstreams as per the established plan.

• Develop and maintain data pipelines and feature engineering workflows for machine learning and AI applications.

• Design, train, evaluate, and optimize machine learning models.

• Implement machine learning solutions for production deployment as microservices, APIs, batch jobs, or streaming components.

• Aid in production monitoring by defining and implementing metrics for model performance, data drift, anomalies, and retraining triggers.

• Collaborate with Data Engineers, Software Engineers, Data Scientists, Product partners, and business stakeholders.

• Contribute to implementation decisions and technical tradeoffs utilizing system design, data models, and technical artifacts.

• Adhere to governance, documentation, coding, and source control standards.

• Assist teammates with their daily responsibilities.

• Record and communicate work progress, technical decisions, and outcomes to both technical and non-technical audiences.

⛳️ Requirements

• A completed BS, MS, or PhD in Computer Science, Mathematics, Statistics, Data Science, Engineering, Operations Research, or a related quantitative field.

• Over 3 years of hands-on experience in building, evaluating, scaling, and deploying machine learning pipelines using Python.

• Proficient in Python programming and a solid understanding of fundamental computer science concepts.

• Experience with Pandas and PySpark.

• Familiarity with scikit-learn, HuggingFace, TensorFlow/Keras, PyTorch, or MLlib.

• Knowledge of MLOps practices, including automated model deployment, model performance monitoring, and data drift detection.

• Working knowledge of SQL and relational data structures.

• Capability to design, train, and evaluate machine learning models following standard best practices.

• Understanding of ETL, ELT, and stream processing methodologies.

• Experience with cloud environments, preferably AWS.

• Familiarity with APIs, microservices, Docker, and Kubernetes.

• Excellent interpersonal, verbal, and written communication skills.

• Ability to work efficiently in a remote setting using collaboration tools.

• Preferred knowledge of recommender systems, fraud detection, personalization, and marketing science.

• Experience in managing and architecting AWS solutions is a plus.

• Familiarity with LLMs, generative AI modalities, and production applications is preferred.

• Familiarity with Snowflake, RDS, DynamoDB, Kafka, Fivetran, dbt, Airflow, Docker, Kubernetes, EMR, SageMaker, Datadog, PagerDuty, data cataloging, data observability, and data governance tools is advantageous.

🏝️ Benefits

• Paid time off (vacation, holidays, sick leave).

• Medical, dental, and vision insurance.

• 401(k) retirement plan.

• Eligibility for a long-term incentive program.

• Remote work opportunities.

• Travel opportunities (approximately 10% of the time).