Senior Machine Learning Engineer

GEICO

. Design, implement, and deploy machine learning models and features for predictive analytics, automation, and decision support .

Posted 9/28/2026full-timeUnited StatesSenior💰 $115,000 - $230,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in designing, implementing, and deploying machine learning models, with a strong focus on the complete ML lifecycle, including data ingestion, feature engineering, and production reliability. Proficient in Python and/or Java, with hands-on experience in ML frameworks and advanced modeling techniques.

Highest-signal resume keywords

Machine Learning Lifecycle ManagementPython and/or Java ProficiencyExperience with ML Frameworks (PyTorch, TensorFlow, scikit-learn)Building and Deploying LLM-based ApplicationsModel Governance and Explainability

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Machine LearningData IngestionFeature EngineeringModel EvaluationProduction IntegrationDeep LearningReinforcement LearningNatural Language ProcessingBig Data Technologies (Spark, Snowflake, Airflow, DBT)Real-time ML Serving Architectures

Soft Skills

Technical LeadershipCollaborationMentoringProblem SolvingCommunication

Tools & Technologies

ML Frameworks (PyTorch, TensorFlow, scikit-learn)Feature StoresModel RegistriesDeployment InfrastructureMonitoring Systems

Industry Keywords

Predictive AnalyticsAutomationDecision SupportAI ResearchDecision IntelligenceModel ExplainabilityFairnessGovernanceComplianceProduction Reliability

Tech Stack

Tools & technologies

AirflowJavaPythonPyTorchScikit-LearnSparkTensorflow

About the role

Key responsibilities & impact

Design, implement, and deploy machine learning models and features for predictive analytics, automation, and decision support
Manage the complete ML lifecycle: data ingestion, feature engineering, training, evaluation, deployment, monitoring, and retraining
Advance model performance, reliability, and interpretability in production environments
Develop scalable batch and real-time pipelines for high-throughput AI workflows and applications
Optimize ML serving systems for speed, reliability, and cost-effectiveness
Collaborate with platform teams to enhance feature stores, model registries, and deployment infrastructure
Author high-quality, well-tested, maintainable Python and/or Java code
Contribute to shared ML frameworks, tooling, and engineering best practices
Engage in architecture discussions, design reviews, and technical roadmap development
Mentor junior engineers through code reviews, guidance, and technical leadership
Collaborate with data scientists, software engineers, operations, and product teams
Translate complex business and technical challenges into actionable ML solutions
Ensure ML systems meet standards for monitoring, alerting, security, privacy, and compliance
Support incident response and maintain production reliability
Contribute to model governance, explainability, and responsible AI practices

Requirements

What you’ll need

Bachelor’s degree in Computer Science, Machine Learning, Statistics, Mathematics, or a related technical field
6+ years of experience building and deploying machine learning systems in production environments
Strong proficiency in Python (and/or Java) and production-grade software engineering practices
Experience with ML frameworks such as PyTorch, TensorFlow, or scikit-learn
Hands-on experience with the full ML lifecycle, including deployment, monitoring, and retraining
Hands-on experience building and deploying LLM-based applications, including prompt design, retrieval-augmented generation (RAG), model evaluation, and production integration
Familiarity with distributed data systems and modern ML infrastructure
Domain experience in AI Research, Predictive Modeling, Automation, or Decision Intelligence
Experience with fine-tuning, adaptation, evaluation frameworks, and safety/guardrails for large language model (LLM) systems
Familiarity with real-time ML serving architectures, feature stores, and low-latency systems
Exposure to advanced modeling techniques, such as deep learning, reinforcement learning, or natural language processing
Experience with big-data and pipeline technologies (Spark, Snowflake, Airflow, DBT)
Knowledge of model explainability, fairness, and governance in regulated industries
GEICO will consider sponsoring a new qualified applicant for employment authorization for this position

Benefits

Comp & perks

Personalized development programs
Mentorship
Certification assistance
Competitive pay
Benefits
Flexibility to support your well-being and future
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