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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.
Distinguished Engineer
GEICO is seeking a Distinguished Engineer (AI Platforms) to join our AI organization. This individual will serve as one of the most senior technical authorities driving the architecture, evolution, and long\-term technical strategy of GEICO’s Generative AI and virtual agent platforms. This platform directly supports productivity and service quality for 20K\+ contact center employees across claims, service, and sales.
In this role, you will operate with enterprise\-wide technical influence, shaping foundational AI platform capabilities that enable large\-scale, production\-grade GenAI and agentic workflows across GEICO. You will work closely with senior engineers, architects, product leaders, and executives to define and evolve durable, scalable, and extensible AI systems that underpin multiple lines of business.
The ideal candidate brings a proven track record of designing and architecting complex, multi\-system AI/ML platforms at scale, deep hands\-on expertise, and a strong passion for Generative AI technologies in real\-world production environments.
Key Responsibilities
Enterprise Architecture \& Technical Authority
- Define, own, and evolve the foundational architecture for GEICO’s Generative AI and agentic workflow platforms.
- Serve as a final technical authority on complex architectural decisions impacting multiple products, teams, and business domains.
- Design interconnected, high\-performance, and durable platform components that power end\-to\-end GenAI workflows, including:
- Knowledge curation and management
- Search and retrieval systems
- Prompt and context management
- Workflow orchestration and action execution
- Semantic and knowledge graph systems
Platform \& GenAI Applications Strategy, Architecture, and Business Impact
- Establish and drive multi\-year technical strategy and roadmaps for both AI platform capabilities and Generative AI (GenAI) applications in close partnership with product and business leaders.
- Balance speed, scalability, reliability, and extensibility while ensuring platforms and GenAI applications can support future AI use cases, evolving business needs, and organizational growth.
- Influence investment decisions by evaluating build vs. buy tradeoffs and architectural choices for both the underlying platform and GenAI applications.
- Define the reference architecture and best practices for GenAI application development, deployment, and integration, ensuring alignment with overall enterprise architecture.
- Collaborate with business stakeholders to identify high\-impact GenAI application opportunities and develop strategies to maximize business value and measurable outcomes.
- Continuously assess and communicate the business impact of GenAI applications, providing clear metrics and feedback loops to inform ongoing strategy and platform evolution.
System \& Technology Evaluation
- Lead evaluation and selection of core technologies, frameworks, and infrastructure components with a emphasis on building and scaling Generative AI applications, including LLM orchestration(e.g., LangChain, LlamaIndex), agentic workflows, RAG systems, and evaluation/observability tooling, while partnering on underling AI platform infrastructure and services to support production readiness.
- Ensure architectural consistency and technical rigor across open\-source, cloud\-agnostic, and managed service integrations.
Cross\-Organization Influence
- Collaborate across engineering, data science, ML, product, and design organizations to align on platform and GenAI application direction, technical standards, and business objectives.
- Drive alignment across teams by translating complex technical and business concepts into clear architectural guidance and decision frameworks.
- Partner with senior technical and business leaders across departments to promote enterprise\-wide adoption of GenAI best practices and maximize organizational impact.
Technical Leadership \& Problem Solving
- Tackle the most complex and ambiguous technical and business challenges affecting system\-wide and application\-specific performance, reliability, and scalability.
- Lead deep technical reviews, architectural assessments, and design discussions for critical AI and GenAI application initiatives.
- Guide platform and GenAI application evolution through hands\-on engagement when necessary, especially in high\-impact or high\-risk areas.
Mentorship \& Technical Stewardship
- Mentor senior engineers and technical leads, setting a high bar for architectural thinking, engineering quality, and technical decision\-making for both platform and GenAI application initiatives.
- Establish and reinforce best practices for platform and GenAI application design, reliability, observability, and operational excellence.
- Contribute to internal documentation, architectural standards, and technical knowledge sharing for both the AI platform and GenAI applications.
Minimum Qualifications
- Master’s degree or higher in Computer Science, Engineering, Statistics, or a related field.
- 10\+ years of professional software engineering experience, with deep expertise in large\-scale distributed systems.
- Extensive experience architecting and building multi\-component AI/ML platforms using technologies such as:
- Search and retrieval systems (e.g., Elasticsearch, Qdrant, Milvus, Pinecone,Weaviate)
- Data platforms (e.g., Snowflake, relational and NoSQL databases, and feature stores)
- Streaming and distributed processing (e.g., Kafka, Spark, Ray)
- Workflow orchestration (e.g., Airflow, Temporal, Prefect)
- LLM orchestration and application frameworks ( e.g., LangChain, Llamaindex)
- Observability, evaluation, and tracing for GenAI systems (e.g., LangSmith, Arize Phoenix, Weights \& Biases, OpenTelemetry)
- Vector databases, embedding pipelines, and retrieval\-augmented generation (RAG) architectures
- Agentic frameworks and multi\-agent systems for complex task execution
- Strong background in full software development lifecycle ownership, including CI/CD, Kubernetes, monitoring, and production operations.
- Deep experience with major cloud platforms such as AWS and Azure.
- Demonstrated hands\-on experience building production systems using LLMs and Generative AI technologies (e.g., GPT, Llama, Mistral, Claude) to power conversational and agentic workflows.
Annual Salary
$210,000\.00 \- $350,000\.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.
At this time, GEICO will not sponsor a new 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 $210K-$350K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $181K across 1996 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 3,824 AI roles we're tracking, AI/ML Engineer positions make up 71% 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 $178,940 based on 11,900 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($280K) sits 56% above the category median. Disclosed range: $210K to $350K.
Across all AI roles, the market median is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $97,380; Mid: $160,000; Senior: $227,400; Director: $243,000; VP: $250,000.
GEICO AI Hiring
GEICO has 21 open AI roles right now. They're hiring across AI Agent Developer, AI/ML Engineer, AI Software Engineer, Research Scientist. Positions span New York, NY, US, Palo Alto, CA, US, Bethesda, MD, US. Compensation range: $215K - $350K.
Location Context
Across all AI roles, 16% (613 positions) offer remote work, while 3,187 require on-site attendance. Top AI hiring metros: New York (2,448 roles, $210,000 median); San Francisco (1,990 roles, $253,000 median); Los Angeles (1,686 roles, $189,000 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 3,824 open positions tracked in our dataset. By seniority: 119 entry-level, 1,813 mid-level, 1,472 senior, and 420 leadership roles (Director, VP, C-Level). Remote roles make up 16% of the market (613 positions). The remaining 3,187 roles require on-site or hybrid attendance.
The market median for AI roles is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($293,500 median, 31 roles); AI Safety ($274,200 median, 51 roles); Research Engineer ($260,000 median, 401 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 3,824 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,702), Data Scientist (281), AI Software Engineer (258). 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 (119) are outnumbered by mid-level (1,813) and senior (1,472) 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 420 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 16% of all AI roles (613 positions), with 3,187 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 $200,000. Top-quartile roles start at $253,000, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $142,800. 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 (1,968 postings), Aws (1,203 postings), Azure (882 postings), Rag (877 postings), Gcp (735 postings), Prompt Engineering (587 postings), Pytorch (586 postings), Claude (554 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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