Staff Machine Learning Engineer, Document & Vision Intelligence

GEICO

Staff ML Engineer building document and vision intelligence systems for GEICO’s auto insurance business. Leading production ML, MLOps, generative AI, and cross-functional delivery.

Posted 8/19/2026full-timeBethesda • California, Maryland, New York • 🇺🇸 United StatesLead💰 $130,000 - $260,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

Use this summary to align your resume positioning with the role.

Demonstrates expertise in designing and implementing machine learning models and services, with a strong foundation in Python, TensorFlow, and cloud platforms like AWS and Azure. Proven ability to lead complex ML projects, mentor junior engineers, and apply modern AI techniques for business solutions.

Highest-signal resume keywords

Machine Learning Model DevelopmentProduction-Grade Code ImplementationCloud Platforms (AWS, Azure)MLOps and CI/CD PipelinesStatistical Modeling and Analysis

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Machine Learning TechniquesPython ProgrammingTensorFlowPyTorchSQLSparkDeep LearningReinforcement LearningNLPGenerative AI

Soft Skills

Excellent Communication SkillsProblem-Solving SkillsAnalytical SkillsBusiness AcumenMentoring and Leadership

Tools & Technologies

KubernetesDockerDatabricksSnowflakeKafka

Industry Keywords

MLOpsModel MonitoringHyperparameter TuningA/B TestingContinuous Deployment

Tech Stack

Tools & technologies

AWSAzureCloudDistributed SystemsDockerGoogle Cloud PlatformKafkaKerasKubernetesPythonPyTorchScikit-LearnSparkSQLTensorflow

About the role

Key responsibilities & impact

Design and implement machine learning models, services, and components for business problems
Write production-grade code for ML models as services and APIs
Collaborate with product, data engineering, software development, and business teams
Build and maintain scalable data processing workflows and model deployment infrastructure
Debug model performance issues, track metrics, and implement continuous improvements
Apply modern ML, generative AI, LLM, agentic workflow, and AI engineering tooling
Lead complex machine learning solutions across business units
Architect infrastructure for automated model training, hyperparameter tuning, and deployment
Mentor and guide junior engineers
Own end-to-end systems for model monitoring, maintenance, and retraining

Requirements

What you’ll need

B.S. in computer science, computer engineering, electrical engineering, machine learning, statistics, mathematics, or a related quantitative field
6+ years of experience applying machine learning techniques such as ensemble learning, deep learning, reinforcement learning, NLP, generative AI, or related approaches
Direct experience designing, building, evaluating, and deploying production-grade ML systems, including model experimentation, evaluation, monitoring, and continuous improvement
6+ years of experience with SQL, Spark or equivalent distributed data processing tools, Python, and machine learning frameworks such as TensorFlow, PyTorch, and Scikit-learn
4+ years of experience with cloud platforms and environments such as AWS, Microsoft Azure, Databricks and/or Snowflake, and Kubernetes
4+ years of experience applying machine learning techniques in a production environment for business solutions
Strong foundation in supervised and unsupervised learning, deep learning, generative AI, and modern AI engineering practices
Proficiency in statistical modeling, probability theory, and hypothesis testing
Proficiency in Python and experience with TensorFlow, Keras, and PyTorch
Familiarity with CI/CD pipelines, Docker, and Kubernetes
Deep understanding of MLOps, model versioning, A/B testing, and continuous deployment
Deep understanding of Azure, AWS, or GCP, distributed systems, Spark, and Kafka
Proven experience leading machine learning projects, managing stakeholders, and scaling ML solutions in production
Excellent communication skills for technical and non-technical audiences
Exceptional problem-solving and analytical skills
Strong product and business acumen
AI-native mindset and ability to leverage LLMs, agents, and modern AI tooling

Benefits

Comp & perks

Personalized development programs
Mentorship
Certification assistance
Competitive pay
Benefits
Flexibility to support well-being and future
Employment authorization sponsorship may be considered for a new qualified applicant