2 days ago
Bethesda, MD, USA +2 moreStaff+
Base Salary
$105k - $215k/yr
Responsibilities
- Design and implement machine learning models, services, APIs, and components for business problems.
- Build scalable data-processing workflows and infrastructure for automated model training, hyperparameter tuning, deployment, monitoring, maintenance, and retraining.
- Integrate machine learning solutions into production systems with product, business, data engineering, and software development teams.
- Debug model performance issues, track metrics, and continuously improve accuracy, reliability, and availability.
- Lead complex machine learning solutions across business units while balancing technical feasibility, product goals, and business impact.
- Mentor junior engineers and provide technical leadership to machine learning engineers and data scientists.
- Apply modern machine learning, generative AI, LLM, agentic workflow, and AI engineering practices to accelerate delivery.
Requirements
- Bachelor's degree in computer science, computer engineering, electrical engineering, machine learning, statistics, mathematics, or a related quantitative field is required; a master's degree or equivalent work experience is preferred.
- At least 3 years of experience applying machine learning techniques such as ensemble learning, deep learning, reinforcement learning, NLP, or generative AI.
- At least 3 years of experience with SQL, Spark or equivalent distributed data processing tools, Python, and machine learning frameworks including TensorFlow, PyTorch, and Scikit-learn.
- At least 3 years of experience with cloud platforms and environments such as AWS, Microsoft Azure, Databricks, Snowflake, and Kubernetes.
- At least 2 years of experience applying machine learning techniques in production business solutions.
- Demonstrated experience designing, building, evaluating, deploying, monitoring, and continuously improving production-grade ML systems.
- Strong knowledge of statistical modeling, probability theory, hypothesis testing, MLOps, distributed systems, large-scale data processing, model versioning, A/B testing, and continuous deployment.
- Experience with software development practices, Docker containerization, Kubernetes orchestration, and CI/CD pipelines.
- Proven ability to lead ML projects, manage stakeholders, communicate technical tradeoffs, and translate ambiguous business needs into execution plans and measurable outcomes.
- Ability to use LLMs, agents, and modern AI tooling to accelerate experimentation, delivery, and decision-making.
Benefits
- GEICO offers personalized development programs, mentorship, and certification assistance.
- GEICO provides competitive pay, benefits, and flexibility to support employee well-being and future needs.
- The company promotes an inclusive, collaborative culture rooted in shared success.
Tech Stack
Apache KafkaApache SparkAWSAzureDatabricksDockerGoogle Cloud PlatformKerasKubernetesPythonPyTorchscikit-learnSnowflakeSQLTensorFlow
Categories
About GEICO
GEICO sells auto and other personal lines insurance to U.S. consumers through a primarily direct-to-consumer model via web, mobile, and phone, with some local agents. Its products include car, motorcycle, RV, boat, homeowners, renters, condo, umbrella, and commercial auto coverage, plus roadside assistance. Founded in 1936 and headquartered in Bethesda, Maryland, GEICO is a subsidiary of Berkshire Hathaway and is among the largest auto insurers in the United States.