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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.
GEICO is seeking a Staff Engineer, Applied AI to help shape how Generative AI enhances customer and associate experiences across the enterprise. This is a senior, hands-on individual contributor role for someone with deep technical expertise, strong collaboration skills, and a proven ability to deliver scalable, resilient, production-ready AI systems.
You will partner with engineering teams, data scientists, and product leaders to design, build, and scale AI-powered capabilities that automate workflows, improve decision-making, and elevate user experience. You will also mentor engineers who want to learn AI, LLMs, and agent-based development, fostering a culture of learning, curiosity, and innovation.
What You Will Do
- Identify and evaluate opportunities for automating business processes using AI, intelligent workflows, and agent-based systems.
- Architect, build, and deploy applied AI solutions across high-value enterprise workflows including automation, document intelligence, decision support, and intelligent assistants.
- Design and implement AI agents and agentic workflows that orchestrate tools, APIs, reasoning steps, and business logic to automate complex processes at scale.
- Build systems and services that meet high standards for scalability, resilience, performance, and availability.
- Use knowledge graphs to enhance reasoning, entity relationships, context retrieval, and multi-step workflows.
- Collaborate with product, engineering, operations, and analytics partners to co-create scalable AI solutions and translate business needs into technical designs.
- Mentor engineers and scientists who want to develop AI and agentic workflow skills through coaching, pairing, reviews, and architectural guidance.
- Drive innovation by exploring new models, frameworks, and reasoning techniques and applying them creatively to real-world challenges.
- Lead through technical influence by providing guidance on architecture, experimentation, and deployment across multiple teams.
- Run rigorous experimentation and evaluation including hypothesis definition, measurement, validation, and iterative improvement in production environments.
- Establish and model engineering best practices for reliability, interpretability, safety, governance, and monitoring of production AI systems.
What We Are Looking For (Must Have)
- 8 or more years of professional software engineering or applied machine learning experience, including 2 or more years working with Generative AI or LLM-based systems in production.
- Strong hands-on experience with Python and modern AI frameworks such as LangChain, LangGraph, LangSmith, LlamaIndex, Hugging Face, and OpenAI or Anthropic APIs.
- Demonstrated experience designing, building, and operating production AI systems including agentic workflows and intelligent automation features.
- Proven experience building scalable, resilient, secure, and maintainable products and systems that run reliably in production.
- Strong understanding of agent architectures, workflow orchestration, retrieval-augmented generation, vector databases, and knowledge graph integration.
- Ability to collaborate deeply across teams and co-create solutions with engineers, product managers, and domain experts.
- Experience mentoring engineers and helping others grow in AI, LLM, and agent-based system design.
- A history of delivering measurable business outcomes from AI systems.
- Strong competency in distributed systems, service design, performance optimization, and reliability engineering.
Nice to Have
- Experience building advanced Generative AI capabilities including domain-tuned LLMs, vector reasoning techniques, or specialized retrieval architectures.
- Experience with insurance, financial services, or other regulated industries.
- Experience deploying AI components in Java ecosystems including Spring AI, LangChain4j, or Embabel.
- Background in document intelligence, fraud or anomaly modeling, or complex ontology and knowledge graph design.
- Familiarity with AI safety practices, evaluation frameworks, monitoring, and regulatory compliance.
- Ability to effectively communicate complex technical topics to senior leadership and non-technical stakeholders.
Who You Are
- A collaborative engineer who works well with cross-functional partners and supports the growth of those around you.
- A hands-on builder who ships reliable, scalable, production-grade systems rather than stopping at proof of concept.
- Someone who understands how to design systems that scale smoothly, recover gracefully, and operate reliably under load.
- An innovative thinker who explores new approaches, tools, and architectures and applies them thoughtfully.
- A natural mentor who enjoys helping others develop AI, LLM, and agentic workflow skills.
- A systems thinker who can connect LLMs, vector search, agents, and knowledge graphs into cohesive solutions.
- Driven by real-world impact, customer value, and long-term engineering excellence.
Why Join GEICO
- Build the next generation of AI-powered enterprise workflows and intelligent automation.
- Work on high-impact problems at large operational scale.
- Collaborate with a talented engineering organization focused on advancing AI capabilities.
- Set the standard for responsible and production-grade AI deployment across the enterprise.
- Mentor and inspire future AI builders across the company.
Annual Salary
$115,000.00 - $230,000.00 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 $115K-$230K range is below the median for AI/ML Engineer roles in our dataset (median: $176K across 1540 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 9,653 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 $212,000 based on 11,857 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $228,700. This role's midpoint ($172K) sits 19% below the category median. Disclosed range: $115K to $230K.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
GEICO AI Hiring
GEICO has 19 open AI roles right now. They're hiring across AI/ML Engineer, AI Agent Developer, AI Software Engineer, AI Product Manager. Positions span Palo Alto, CA, US, Seattle, WA, US, New York, NY, US. Compensation range: $230K - $300K.
Location Context
AI roles in Seattle pay a median of $226,100 across 973 tracked positions. That's 6% above the national 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 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 $253,100 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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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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