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GEICO

Senior Staff ML Engineer

GEICO

. Architect and implement scalable, high-performance machine learning platforms and systems for large data volumes, real-time decision-making, and workflows .

Posted 10/6/2026full-timeUnited StatesSenior💰 $150,000 - $300,000 per yearWebsite

Core Competencies

Role fit
Core Competencies

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Demonstrates expertise in architecting and implementing scalable machine learning platforms, managing the end-to-end AIML lifecycle, and optimizing data pipelines. Proficient in translating complex technical concepts into actionable insights while fostering collaboration and innovation across teams.

Highest-signal resume keywords
AIML System DesignLarge-Scale Data Pipeline ArchitecturePython ProgrammingAWS Machine Learning ServicesTensorFlow Framework

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills
Machine LearningData IngestionFeature EngineeringModel TrainingModel DeploymentPerformance TuningTechnical DocumentationModel MonitoringAutomated RetrainingComplex Problem-Solving
Soft Skills
MentorshipCollaborationInnovationCommunicationTechnical Leadership
Tools & Technologies
AirflowDBTKubernetesSparkMongoDBSnowflakeNeo4jRedisSageMakerAzure ML
Certifications & Qualifications
Bachelor’s Degree in Machine LearningAdvanced Degree in Related Field
Industry Keywords
Real-Time Decision-MakingModel InterpretabilityRegulatory ComplianceFraud DetectionRisk ModelingDigital IdentityGraph Neural NetworksModel GovernanceExplainabilityBias Mitigation

Tech Stack

Tools & technologies
AirflowAWSAzureCloudJavaKubernetesMongoDBNeo4jPythonPyTorchRedisScikit-LearnSparkTensorflow

About the role

Key responsibilities & impact
  • Architect and implement scalable, high-performance machine learning platforms and systems for large data volumes, real-time decision-making, and workflows
  • Design end-to-end AIML pipelines from data ingestion and feature engineering through model training, deployment, and continuous monitoring
  • Evaluate and integrate AIML frameworks and libraries
  • Act as technical lead across multiple ML feature teams and set technical direction
  • Provide hands-on mentorship and guidance during design reviews, code assessments, and performance tuning
  • Lead complex technical problem-solving and system-wide architectural improvements
  • Experiment with and prototype advanced machine learning algorithms and approaches
  • Translate research and industry trends into production-level solutions
  • Contribute to technical documentation and share knowledge across teams
  • Oversee the end-to-end machine learning model lifecycle, including testing, deployment, and monitoring
  • Develop model monitoring, alerting, and automated retraining systems
  • Ensure industry standards, security protocols, and regulatory compliance throughout the ML lifecycle
  • Collaborate with data scientists, software engineers, operations, and product teams to integrate ML systems into production
  • Translate complex technical concepts into actionable insights for technical and non-technical stakeholders
  • Foster collaboration, innovation, and sharing of best practices across teams

Requirements

What you’ll need
  • Bachelor’s degree in Machine Learning, Computer Science, Statistics, Mathematics, or a related field
  • 15+ years of hands-on experience designing, implementing, and optimizing AIML systems in production environments
  • Extensive expertise architecting large-scale data pipelines, real-time AIML serving architectures, and managing the end-to-end AIML lifecycle
  • Proven ability to tackle complex technical challenges, innovate through hands-on experimentation, and set technical standards across teams
  • Deep proficiency in Python, Java, or similar programming languages, with emphasis on coding excellence
  • Experience with Airflow, DBT, Kubernetes, and big-data technologies such as Spark, MongoDB, Snowflake, Neo4j, and Redis
  • Familiarity with modern data feature stores
  • Significant experience with AWS, Azure, or similar cloud platforms and machine learning services such as SageMaker and Azure ML
  • Familiarity with model interpretability, fairness, and regulatory compliance frameworks
  • Proficiency in TensorFlow, PyTorch, Scikit-learn, or similar machine learning frameworks
  • Advanced degree is highly desirable
  • Preferred: experience in Fraud Detection, Risk Modeling, Trust and Safety, or Digital Identity
  • Preferred: experience deploying LLMs in production, including RAG and fine-tuning, or building Graph Neural Networks
  • Preferred: model governance, explainability, and bias mitigation experience in a regulated industry

Benefits

Comp & perks
  • Personalized development programs
  • Mentorship
  • Certification assistance
  • Competitive pay
  • Benefits
  • Flexibility to support your well-being and future
  • Employment authorization sponsorship consideration for a new qualified applicant