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 to implementation and delivery.

• Convert technical requirements into high-quality, maintainable code and execute workstreams in accordance with the project timeline.

• Develop and sustain data pipelines and feature engineering workflows to facilitate machine learning and AI solutions.

• Design, train, evaluate, and optimize machine learning models independently, applying sound statistical and engineering methodologies.

• Implement machine learning solutions that can be integrated into production environments as microservices, APIs, batch jobs, or streaming components.

• Assist in production monitoring initiatives by defining and implementing metrics related to model performance, data drift, anomalies, and retraining triggers.

• Work collaboratively with Data Engineers, Software Engineers, Data Scientists, Product partners, and business stakeholders to achieve project goals.

• Possess a strong understanding of system design, data models, and technical artifacts to effectively contribute to implementation decisions and tradeoffs.

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

• Exhibit flexibility and proactively assist teammates with daily responsibilities as required.

• Clearly document and communicate progress, technical decisions, and results to both technical and non-technical audiences.

⛳️ Requirements

• Bachelor's, Master's, or PhD degree in Computer Science, Mathematics, Statistics, Data Science, Engineering, Operations Research, or another quantitative discipline.

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

• Proficient programming skills in Python and a solid grasp of core computer science concepts.

• Experience with data manipulation frameworks like Pandas and PySpark.

• Familiarity with machine learning libraries such as scikit-learn, HuggingFace, TensorFlow/Keras, PyTorch, or MLlib.

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

• Competent in SQL and relational data structures.

• Understanding of batch and streaming data pipeline concepts such as ETL, ELT, and stream processing.

• Experience working in cloud environments, preferably AWS.

• Familiarity with technologies such as APIs, microservices, Docker, and Kubernetes.

• Excellent interpersonal, verbal, and written communication skills.

• Ability to work efficiently in a remote environment utilizing collaboration tools.

🏝️ Benefits

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

• Medical, dental, and vision insurance.

• 401(k) retirement plan.

• Long-term incentive programs.