GEICO

Product Manager

• Lead Service data initiatives to align data sources and definitions.
• Collaborate with stakeholders from all departments.
• Ensure accurate tracking and reporting of data attributes.
• Drive projects to enhance data quality and accessibility.

Data Improvement Product Manager

General Counsel

• Manages subordinates in all activities relating to the defense of lawsuits and against GEICO insureds in liability cases, and on behalf of GEICO in UM/UIM, PIP and Subrogation suits.
• INTERVIEWS and/or APPROVES job applicants for employment.
• CONDUCTS and/or REVIEWS associate Performance Appraisals.
• INITIATES or APPROVES salary adjustments, performance ratings, and other personnel changes.
• COUNSELS associates and TAKES disciplinary action or TERMINATES the employment of associates as appropriate.
• May represent GEICO insureds in liability cases, UM/UIM, and PIP suits filed in courts of limited and unlimited jurisdiction.
• RESEARCHES laws and PREPARES legal briefs, opinions, and memoranda.
• RENDERS opinions on liability, damages, and value as requested by the Claims Department.
• May prepare and handle pleadings, motions, and discovery, to include depositions/examinations before trial and examinations under oath, and other deadlines.
• TRAINS and SUPERVISES less experienced attorneys, including assisting attorneys as first and second chair counsel, and/or observing attorneys at trials and arbitrations;
• MONITORS all applicable bar requirements including mandatory Continuing Legal Education (“CLE”) requirements;
• PROVIDES feedback on quality of file handling and expense management.
• REVIEWS office reports and IMPLEMENTS changes to improve office statistics, including timeliness of reports to clients, productivity reports, client and claims survey results, resolved ratio, and subrogation results.
• ADHERES to the GEICO Code of Conduct, company policies, and operating principles.
• MEETS attendance standard of the business location, to perform necessary job functions and to facilitate interaction with subordinates and management.
• MEETS the requirements specified below.
• Must be able to travel as required, including but not limited, to attend trials, hearings, depositions, management meetings and conferences.

Senior Manager, Staff Counsel

Machine Learning Engineer

• Leverage advanced research and technology expertise through individual assignments and collaborative team projects
• Contribute to technology business initiatives and present outcomes to senior management
• Engage with associates and help shape the future of AI-driven insurance solutions
• Strengthen research, technical, project management, and communication skills in a dynamic environment

AI Applied Research, Machine Learning Internship – PhD

Backend Engineer

• Scope, design, and build scalable, resilient distributed systems
• Engage in cross-functional collaboration throughout the entire software lifecycle
• Participate in design sessions and code reviews
• Utilize programming languages like Go, Python, Java, and others
• Consistently share best practices and improve processes
• Build product definition and leverage technical skills for solutions

Full Stack Engineer II – Java, React, Postgres, Microservices

Full-stack Engineer

• Lead the architecture and solution design of a vendor product or bespoke system to conduct complex calculations across multiple dimensions to support advanced Pricing processes and analytics.
• Leverage your knowledge and experience across a broad range of technologies to ensure end-to-end solutions are sound, scalable and capable of meeting both current and future business requirements.
• Collaborate with stakeholders to align technology solutions with business goals and requirements.
• Ensure seamless integration of financial, business and reference data from various sources to support the complex dimensional reporting and analytic requirements of Pricing systems.
• Lead and mentor other engineers, consistently share best practices, and improve processes within and across teams.
• Innovate and prototype new tools or frameworks to improve development efficiency and product quality.
• Provide thought leadership in new technologies, design patterns, and best practices, staying ahead of industry trends and emerging technologies.
• Perform root cause analysis of performance issues and implement monitoring, profiling, and optimization strategies as needed.

Staff Engineer – Pricing Technologies

• Lead software design, development, and delivery of integrated systems to drive Vulnerability Management initiatives.
• Deliver automation initiatives, conduct advanced research, and develop proofs of concept to enhance our capabilities and improve overall efficiency.
• Achieve business outcomes through force multiplication.
• Develop, integrate, and maintain multilevel cybersecurity designs, architectures, policies, and procedures.
• Provide secure design guidance and recommendations to developers, infrastructure, and product engineers.
• Influence and educate partner teams to bring an engineering first approach to develop sustainable security systems.
• Mentor peers and team members in security technologies, enterprise solution design, deployment, and effective customer interaction.
• Provide motivating demonstrations and communications to show the value of our security measures to the business, highlighting the low impact on systems, improved operability and resiliency.

Staff Software Engineer, Vulnerability Management

Security Engineer

• Lead the full vulnerability lifecycle: discovery, validation, risk analysis, prioritization, and remediation measurement
• Leverage business contextualization, underlying systems, and threat intelligence to perform risk assessment for identifying true risk to drive remediation
• Build integrations among scanning tools, asset inventory, CMDBs, ticketing, CI/CD, and monitoring pipelines to streamline workflows
• Evaluate, test, and implement emerging tools and technologies that advance VM automation and intelligence
• Design automation to reduce manual work, increase accuracy, and accelerate remediation
• Generate data-driven insights that help teams understand, prioritize, and resolve vulnerabilities efficiently
• Collaborate with cloud, infrastructure, DevOps, and product engineering groups to integrate vulnerability management into pipelines and delivery workflows
• Work closely with risk, compliance, governance, and incident response teams to ensure alignment with organizational and regulatory standards
• Communicate vulnerability trends, risk implications, and remediation strategies to technical and non-technical stakeholders
• Define KPIs, SLAs, dashboards, and reporting models to drive accountability and measurable vulnerability reduction
• Establish repeatable processes, playbooks, and workflows that ensure consistent VM operations across teams and environments
• Ensure the reliability, performance, and scalability of VM tools and data pipelines
• Mentor junior and mid-level engineers, offering guidance on advanced security concepts, engineering best practices, and career development
• Serve as a multiplier by elevating skillsets across teams through coaching, pairing, design reviews, and knowledge-sharing

Senior Staff Security Engineer – Vulnerability Management

Research Scientist

• Identify High-Impact Opportunities: Proactively surface and shape high-value AI/ML initiatives by engaging with product, engineering, and operations to align technical roadmaps with strategic business goals.
• Architecture & Technical Direction: Provide architectural leadership for AI/ML solutions impacting multiple stakeholders. Establish standards for scalability, reliability, observability, compliance, and cost efficiency across online and batch systems.
• Development & Productionization: Lead end-to-end delivery of AI/ML solutions, including model design, data pipelines, feature stores, evaluation, deployment, A/B testing, and monitoring in real-time and batch environments. Ensure clear plans, milestones, and on-time delivery.
• ROI Measurement & Experimentation: Establish robust mechanisms to quantify business impact, including KPI definition, experimentation frameworks, and causal inference approaches to guide decision-making and prioritize investments.
• Innovation & Research Integration: Stay current with cutting-edge research in ML, GenAI, and optimization. Prototype and harden novel techniques that push the boundaries of innovation within GEICO’s insurance ecosystem. Set technical direction for multi-quarter research initiatives; build evaluation frameworks, ensure reproducibility/responsible AI, and drive cross-functional adoption; shepherd patents.
• Cross-Functional Collaboration: Champion collaboration across Product, Engineering, Data Platform, Governance, Legal, and Operations to ensure responsible, compliant, and effective adoption of AI systems.
• Mentorship & Capability Building: Mentor junior and senior scientists, elevate technical standards (coding, testing, documentation, reproducibility), and foster a culture of scientific rigor and engineering excellence.
• Communication & Executive Engagement: Translate complex technical topics into clear narratives for technical and non-technical audiences. Present to senior stakeholders, set context, explain trade-offs, and build alignment and enthusiasm.

Staff Applied Research Scientist – Martech

• Design and implement scalable infrastructure for training, fine-tuning, and serving open source LLMs (Llama, Mistral, Gemma, etc.)
• Architect and manage Kubernetes clusters for ML workloads, including GPU scheduling, autoscaling, and resource optimization
• Design, implement, and maintain feature stores for ML model training and inference pipelines
• Build and optimize LLM inference systems using frameworks like vLLM, TensorRT-LLM, and custom serving solutions
• Ensure 99.9%+ uptime for ML platforms through robust monitoring, alerting, and incident response procedures
• Design and implement ML platforms using DataRobot, Azure Machine Learning, Azure Kubernetes Service (AKS), and Azure Container Instances
• Develop and maintain infrastructure using Terraform, ARM templates, and Azure DevOps
• Implement cost-effective solutions for GPU compute, storage, and networking across Azure regions
• Ensure ML platforms meet enterprise security standards and regulatory compliance requirements
• Evaluate and potentially implement hybrid cloud solutions with AWS/GCP as backup or specialized use cases
• Mentor junior engineers and data scientists on platform best practices, infrastructure design, and ML operations
• Lead comprehensive code reviews focusing on scalability, reliability, security, and maintainability
• Design and deliver technical onboarding programs for new team members joining the ML platform team
• Work closely with data scientists to understand requirements and optimize workflows for model development and deployment

Senior Staff Software Engineer – AI/ML Infra

• Lead highly effective large-scale penetration testing initiatives.
• Participate in simulating real-world cyber-attacks (red teaming), and collaborating with defensive security teams (purple teaming).
• Conduct tactical security penetration test assessments to validate the security of company applications (web, mobile, APIs, and AI products) against OWASP Top 10 threats and work with the Application Security team to provide feedback and recommendations to increase automated capabilities.
• Ensure penetration testing activities are meeting security, business, and compliance objectives and outcomes.
• Design and execute advanced threat emulation scenarios, including physical, social, and digital attack vectors.
• Collaborate with Blue Teams, Threat Intelligence, and Risk Management to ensure comprehensive attack coverage and feedback loops.
• Ensure operations align with industry regulations and compliance standards such as NIST, PCI DSS, and NYDFS.
• Champion continuous improvement and innovation in penetration testing, adversary simulation techniques, tools, and methodologies.

Staff Offensive Security Engineer – Hybrid

Analyst

• Under general supervision, supports the rollout of new and revised insurance products by providing model documentation, coordinating filing objections, and owning objections related to product design and modeling impacts.
• Partners closely with Research/Modeling, Pricing, State Management, and Product Deployment teams to ensure regulatory readiness and successful implementation across multiple lines of business.
• Provides GLM and model documentation in support of new and revised insurance product filings, ensuring clarity, accuracy, and regulatory readiness.
• Owns and coordinates product filing objections, with primary responsibility for objections related to product structure, rating, and modeling impacts; partners with internal teams to drive timely resolution.
• Supports new product rollouts and post-filing updates, including product modifications required as a result of regulatory feedback or objections.
• Works cross-functionally with Research/Modeling, Pricing, State Management, Legal, and Product Deployment teams to review and refine business requirements associated with filings and objections.
• Executes within well-defined projects while operating independently, managing assigned responsibilities end-to-end and escalating issues as appropriate.
• Supports leadership by providing clear analysis, documentation, and recommendations related to filings, objections, and product changes.
• Contributes to process improvement efforts, including the development of automated and standardized approaches to objection handling and model documentation.

Product Deployment Analyst II

• Design and Develop Systems: Architect, develop, and maintain scalable, resilient distributed systems.
• Define Product Requirements: Collaborate with stakeholders to define product requirements and deliver optimal solutions.
• Collaborate Across Teams: Work with cross-functional teams throughout the software development lifecycle to ensure seamless integration and delivery.
• Lead and Mentor: Conduct design sessions, code reviews, and mentor junior engineers to elevate engineering quality and share best practices.
• Develop Reusable Components: Create reusable application components and patterns to improve efficiency and scalability.
• Full-Stack Development: Design, develop, and maintain scalable full-stack applications, including front-end features using ReactJS and its ecosystem (e.g., Redux, Hooks), backend services with .NET or Java frameworks, and efficient data management using SQL and NoSQL databases.
• API and Microservices: Develop and integrate RESTful APIs and microservices for seamless communication between systems.
• DevOps Practices: Utilize CI/CD pipelines, containerization (Docker), and orchestration (Kubernetes) to streamline development and deployment.
• Documentation: Publish and maintain documentation for design references, workflows, and runbooks.
• System Support: Provide support for critical systems, including 24x7 on-call support when necessary.

Senior Full Stack Engineer

• Own ML platform architecture: data/feature pipelines, experiment tracking, model registries, serving layers, offline/online evaluation, and observability
• Define standards for reliability, performance, cost efficiency, security, governance, and model risk management across ML services
• Lead design and implementation of models across classical ML and deep learning
• Build scalable training and inference pipelines; establish CI/CD for ML
• Implement monitoring for data quality, drift, fairness, latency, reliability, and cost
• Mentor engineers and junior tech leads; codify best practices

Senior Staff Machine Learning Engineer

• Identify High-Impact Opportunities: Proactively surface and shape high-value AI/ML initiatives by engaging with product, engineering, and operations to align technical roadmaps with strategic business goals.
• Architecture & Technical Direction: Provide architectural leadership for AI/ML solutions impacting multiple stakeholders.
• Development & Productionization: Lead end-to-end delivery of AI/ML solutions, including model design, data pipelines, feature stores, evaluation, deployment, A/B testing, and monitoring in real-time and batch environments.
• ROI Measurement & Experimentation: Establish robust mechanisms to quantify business impact.
• Innovation & Research Integration: Stay current with cutting-edge research in ML, GenAI, and optimization.
• Mentorship & Capability Building: Mentor junior and senior scientists, elevate technical standards and foster a culture of scientific rigor and engineering excellence.
• Communication & Executive Engagement: Translate complex technical topics into clear narratives for technical and non-technical audiences.

Staff Applied Research Scientist – Fraud Detection

• Work on the ML platform architecture: data/feature pipelines, experiment tracking, model registries, serving layers, offline/online evaluation, and observability.
• Define standards for reliability, performance, cost efficiency, security, governance, and model risk management across ML services.
• Lead design and implementation of models across classical ML and deep learning (e.g., gradient boosted trees, sequence models, Transformers for tabular/time-series/NLP where relevant).
• Translate business goals into measurable ML objectives and experiment plans; ensure robust offline metrics and real-world impact.
• Build scalable training and inference pipelines; establish CI/CD for ML, automated evaluations, canary releases, and rollback strategies.
• Implement monitoring for data quality, drift, fairness, latency, reliability, and cost; lead incident response and postmortems.
• Partner with Claims, Product, Data Science, Platform/SRE, Security, and Legal/Compliance to gather requirements, define scope, and prioritize backlogs.
• Maintain pragmatic technical roadmaps balancing business outcomes, release timelines, and engineering excellence.
• Own build-vs-buy decisions and tooling/service selection (speed to market, extensibility, TCO); guide platform evolution with clear architectural principles.
• Lead experienced engineers through complex platform implementations; drive system-wide architectural improvements and reliability practices.
• Mentor engineers and junior tech leads; codify best practices; contribute to internal documentation and promote enterprise-wide ML standards.
• Where appropriate, collaborate on retrieval-augmented workflows, prompt/context management, and LLM evaluation and safety guardrails to complement ML systems.