Design, implement, and deploy end-to-end machine learning solutions.
Leverage expertise in machine learning, software engineering, and system architecture to independently drive the development of production-ready models.
Work closely with cross-functional teams, from product and business units to data scientists, ensuring seamless integration of ML models into business-critical applications.
Independently design, implement, deploy, and maintain machine learning models and components that solve real-world business problems in close collaboration with the Product, Business units, and Data Science teams.
Write production-grade code for ML models as services and APIs.
Collaborate with cross-functional teams including data engineering and software development, to integrate machine learning models into production systems.
Build and maintain scalable data processing workflows and model deployment infrastructure.
Debug and resolve model performance issues, track relevant metrics, and implement continuous improvements to ensure model accuracy and reliability.
Collaborate with PM, Design, Product Engineering, and Data Science teams to ensure end-to-end business impact.

Bachelor’s degree in Machine Learning, Computer Science, Statistics, Mathematics, or a related quantitative field.
Two (2) years of experience as a Software Engineer, or related occupation.
Two (2) years of experience with: Developing and deploying machine learning models in production environments, with expertise in a variety of ML techniques;
Writing production-grade code and creating APIs using frameworks such as TensorFlow, PyTorch, or Scikit-learn;
Cloud-based environments and familiarity with containerization and orchestration tools;
Building data processing and ML workflow pipelines using SQL, Spark, and Python scripting;
Utilizing distributed computing frameworks and large-scale data processing tools;
ML algorithm foundations;
Engineering skills to develop production ML/AI systems;
Transforming business problems into ML/AI problems;
Advanced machine learning algorithms and techniques, including supervised, unsupervised, and generative models;
Python;
ML frameworks, such as TensorFlow, Keras, and PyTorch;
Software development best practices, such as CI/CD, containerization, and version control;
Cloud platforms (AWS, Azure, GCP) and ability to leverage them for scalable and efficient ML solutions;
Data engineering concepts, including building scalable ETL pipelines, working with big data tools (Spark, Kafka), and ensuring smooth data flow for ML workflows.

Comprehensive Total Rewards program that offers personalized coverage tailor-made for you and your family’s overall well-being.
Financial benefits including market-competitive compensation; a 401K savings plan vested from day one that offers a 6% match; performance and recognition-based incentives; and tuition assistance.
Access to additional benefits like mental healthcare as well as fertility and adoption assistance.
Supports flexibility
We provide workplace flexibility as well as our GEICO Flex program, which offers the ability to work from anywhere in the US for up to four weeks per year.

Machine Learning Engineer II

Key skills