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About This Role
Why Join GEICO?
At GEICO, we offer a rewarding career where your ambitions are met with endless possibilities.
Every day we honor our iconic brand by offering quality coverage to millions of customers and being there when they need us most. We thrive on relentless innovation to exceed our customers' expectations while making a real impact on local communities nationwide.
Founded in 1936, GEICO is a member of the Berkshire Hathaway family of companies and one of the largest auto insurers in the United States. When you join our company, we want you to feel valued, supported, and proud to work here. That's why we offer the GEICO Pledge: Great Company, Great Culture, Great Rewards, and Great Careers.
Overview: GEICO is on a journey to transform the insurance industry with Artificial Intelligence. The Fraud Risk Modeling team is at the center of this evolution. We are not just building models; we are architecting a centralized multi-modal fraud defense ecosystem that protects millions of customers. As a Senior Staff Machine Learning Engineer, you will be a technical anchor for the Fraud Risk Modeling team. You will partner with other AIML teams in building coherent, real-time fraud platform and solutions that unify claims, payment, and identity risk assessment. This is a high-impact role for a builder who cares about architectural elegance, system reliability, and is able to solve complex, large-scale, cross functional problems. This role requires a minimum of 15 years of relevant experience.
Key Responsibilities:
- Technical Architecture & System Design: + Architect and implement scalable, high-performance machine learning platforms and systems capable of processing large data volumes and supporting real-time decision making and workflows. + Design end-to-end AIML pipelines - from data ingestion and feature engineering to model training, deployment, and continuous monitoring. + Evaluate and integrate cutting-edge AIML frameworks and libraries to maintain a state-of-the-art technology stack.
- Technical Leadership & Expert Guidance: + Act as the tech lead across multiple ML feature teams, setting technical direction and ensuring consistency in design principles and best practices. + Provide hands-on mentorship and guidance during design reviews, code assessments, and performance tuning. + Lead by example in tackling complex technical challenges and driving system-wide architectural improvements.
- Innovation & Research Integration: + Experiment with and prototype advanced machine learning algorithms and approaches to enhance system performance, model accuracy, and interpretability. + Stay abreast of the latest research and industry trends, translating these insights into actionable, production-level solutions. + Contribute to internal technical documentation and share knowledge across teams.
- Lifecycle Management & Reliability: + Oversee the end-to-end lifecycle of machine learning models, ensuring robust testing, deployment, and ongoing monitoring. + Develop and implement systems for model monitoring, alerting, and automated retraining to maintain peak performance in production. + Ensure adherence to industry standards, security protocols, and regulatory compliance throughout the ML lifecycle.
- Cross-Functional Collaboration: + Work closely with data scientists, software engineers, operations, and product teams to seamlessly integrate ML systems into production environments. + Translate complex technical concepts into actionable insights for both technical and non-technical stakeholders. + Foster a collaborative environment that encourages innovation and the sharing of best practices across teams.
Minimum Qualifications:
- Bachelor’s degree in Machine Learning, Computer Science, Statistics, Mathematics, or a related field; an advanced degree (master’s or Ph.D.) is highly desirable.
- 15+ years of hands-on experience in designing, implementing, and optimizing AIML systems in production environments.
- Extensive expertise in 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 programming languages such as Python, Java, or similar, with a strong emphasis on coding excellence.
- Experience with backend distributed systems & tools (e.g., Airflow, DBT, Kubernetes) and big-data technologies (e.g., Spark, MongoDB, Snowflake, Neo4j, Redis), familiarity with modern data feature stores.
- Significant experience working with cloud platforms (AWS, Azure, etc.) and their machine learning services (e.g., SageMaker, Azure ML, etc.).
- Familiarity with frameworks for model interpretability, fairness, and regulatory compliance, ensuring ethical and transparent ML systems.
- Proficiency in machine learning frameworks such as TensorFlow, PyTorch, Scikit-learn, etc.
Preferred Qualifications:
- Domain expertise: prior experience in Fraud Detection, Risk Modeling, Trust and Safety, or Digital Identity.
- Advanced ML techniques: experience deploying LLM in production (RAG, fine-tuning) or building Graph Neural Networks for network analysis.
- Governance: experience with model governance, explainability, and bias mitigation in a regulated industry like insurance.
If you are passionate about pushing the boundaries of machine learning technology, thrive in a hands-on technical leadership role, and enjoy solving complex, large-scale problems, we encourage you to apply.
Annual Salary
$150,000.00 - $300,000.00 The above annual salary range is a general guideline. Multiple factors are taken into consideration to arrive at the final hourly rate/ annual salary to be offered to the selected candidate. Factors include, but are not limited to, the scope and responsibilities of the role, the selected candidate’s work experience, education and training, the work location as well as market and business considerations.
GEICO will consider sponsoring a new qualified applicant for employment authorization for this position. The GEICO Pledge:
Great Company: Protecting customers through life’s twists and turns with innovation and integrity.
Great Careers:Personalized development programs, mentorship, and certification assistance.
Great Culture:Inclusive and collaborative culture rooted in shared success.
Great Rewards:Competitive pay, benefits, and flexibility to support your well-being and future.
The equal employment opportunity policy of the GEICO Companies provides for a fair and equal employment opportunity for all associates and job applicants regardless of race, color, religious creed, national origin, ancestry, age, gender, pregnancy, sexual orientation, gender identity, marital status, familial status, disability or genetic information, in compliance with applicable federal, state and local law. GEICO hires and promotes individuals solely on the basis of their qualifications for the job to be filled.
GEICO reasonably accommodates qualified individuals with disabilities to enable them to receive equal employment opportunity and/or perform the essential functions of the job, unless the accommodation would impose an undue hardship to the Company. This applies to all applicants and associates. GEICO also provides a work environment in which each associate is able to be productive and work to the best of their ability. We do not condone or tolerate an atmosphere of intimidation or harassment. We expect and require the cooperation of all associates in maintaining an atmosphere free from discrimination and harassment with mutual respect by and for all associates and applicants.
Salary Context
This $150K-$300K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $178K across 1623 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At GEICO, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $211,950 based on 13,048 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $229,300. This role's midpoint ($225K) sits 6% above the category median. Disclosed range: $150K to $300K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
GEICO AI Hiring
GEICO has 14 open AI roles right now. They're hiring across AI/ML Engineer, AI Agent Developer, AI Software Engineer, Research Scientist. Positions span Bethesda, MD, US, Palo Alto, CA, US, New York, NY, US. Compensation range: $230K - $300K.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 roles, $210,000 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (106) are outnumbered by mid-level (4,384) and senior (3,343) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $256,000 median, while Prompt Engineer roles sit at $141,440. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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