Staff AI/ML Engineer

Staff/Sr. Staff AI/ML Engineer –

Overview:

GEICO is seeking an experienced Staff or Sr. Staff Machine Learning Engineer to join the AI organization. This person will take on a critical leadership role in designing, implementing, and deploying cutting-edge machine learning models that solve real-world business challenges. You will collaborate with various business units to build scalable, high-performance ML systems with a strong emphasis on system design. In addition to technical contributions, you will mentor junior engineers, drive the full lifecycle of machine learning model development, and ensure that models are seamlessly integrated into production. This position requires expertise in both machine learning and software engineering to develop robust, production-grade solutions.

Key Responsibilities:

• Lead the Design & Implementation of ML Models: Lead the architecture and implementation of machine learning models, working closely with Product, Business Units, and Engineering teams.
• Build Scalable Infrastructure: Design and develop scalable infrastructure for model training, automated hyperparameter tuning, and deployment pipelines, ensuring that systems are reliable and performant at scale.
• Write Production-Grade Code for ML Services and APIs: Write high-quality, maintainable production-grade code that turns machine learning models into deployable services and APIs. Ensure that code is modular and reusable for future ML projects.
• Optimize Model Performance and Resolve Issues: Debug and troubleshoot model performance issues, track key metrics, and continuously enhance model reliability, speed, and efficiency in production environments.
• End-to-End Model Lifecycle Management: Own the complete lifecycle of ML models, including monitoring, retraining, and managing versions of models to ensure they continue to meet business needs over time.
• Leadership and Mentorship: Guide and mentor junior machine learning engineers, promote best practices in software engineering, model development, and deployment. Lead technical decision-making processes and foster collaboration within the team.
• Collaboration Across Teams: Collaborate with cross-functional teams (e.g., data engineering, software development, and product management) to integrate machine learning models and ensure smooth deployment and operations in production systems.
• Stay Up to Date with Industry Trends: Continuously explore and integrate new machine learning techniques and system engineering tools, ensuring the team remains at the forefront of machine learning and systems architecture practices.

Basic Qualifications:

• B.Sc. in Computer Science, Machine Learning, Engineering, or a related technical field.
• 6+ years of hands-on experience applying machine learning techniques, including deep learning, reinforcement learning, and NLP in production environments.
• 6+ years of experience utilizing open-source/cloud-agnostic components such as data warehouse (e.g. snowflake), streaming platform (e.g. Kafka), relational database (e.g. PostgreSQL), NoSQL (e.g. MongoDB, Cassandra), distributed processing (e.g. Spark, Ray), workflow management (e.g. Airflow, Temporal), etc.
• 6+ years of professional software development experience with at least two general-purpose programming languages such as Java, C++, Python or C#.
• 6+ years of experience with machine learning frameworks such as TensorFlow, PyTorch, Scikit-learn for model development.
• At least 4 years of experience with cloud platforms (AWS, Azure, GCP) and containerization technologies such as Docker, as well as orchestration tools like Kubernetes.
• Proven experience in deploying machine learning models in a production environment, ensuring scalability, reliability, and high availability.

Core Engineering Skills & Knowledge:

• Extensive experience with object-oriented design (OOD), design patterns, writing clean, and maintainable code. Proficiency in version control (Git) and familiarity with Agile methodologies.
• Solid understanding of distributed systems and the challenges associated with scaling machine learning models in production, such as managing distributed data processing and microservices architectures.
• Expertise in implementing MLOPs practices, including setting up continuous integration (CI), continuous delivery (CD), automated testing, and deployment pipelines for ML models.
• Strong understanding of system architecture, performance optimization, designing fault-tolerant systems that handle large-scale data and high-volume requests.
• Experience designing and deploying machine learning models using cloud-based environments like AWS, Azure, or Google Cloud. Familiarity with cloud-native