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About This Role
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 through relentless innovation to exceed our customers’ expectations while making a real impact for our company through our shared purpose.
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 AI platform and Infrastructure team is seeking an exceptional Senior ML Platform Engineer to build and scale our machine learning infrastructure with a focus on Large Language Models (LLMs) and AI applications. This role combines deep technical expertise in cloud platforms, container orchestration, and ML operations with strong leadership and mentoring capabilities. You will be responsible for designing, implementing, and maintaining scalable, reliable systems that enable our data science and engineering teams to deploy and operate LLMs efficiently at scale. The candidate must have excellent verbal and written communication skills with a proven ability to work independently and in a team environment.
KEY RESPONSIBILITIES
ML Platform \& Infrastructure
- 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
DevOps \& Platform Engineering
- Design and maintain robust CI/CD pipelines for ML model deployment using Azure DevOps, GitHub Actions, and MLOps tools
- Implement automated model training, validation, deployment, and monitoring workflows
- Set up comprehensive observability using Prometheus, Grafana, Azure Monitor, and custom dashboards
- Continuously optimize platform performance, reducing latency and improving throughput for ML workloads
- Design and implement backup, recovery, and business continuity plans for ML platforms
Technical Leadership \& Mentoring
- 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
- Establish and champion engineering standards for ML infrastructure, deployment practices, and operational procedures
- Create technical documentation, runbooks, and deliver internal training sessions on platform capabilities
Cross\-Functional Collaboration
- Work closely with data scientists to understand requirements and optimize workflows for model development and deployment
- Collaborate with product engineering teams to integrate ML capabilities into customer\-facing applications
- Support research teams with infrastructure for experimenting with cutting\-edge LLM techniques and architectures
- Present technical solutions and platform roadmaps to leadership and cross\-functional stakeholders
REQUIRED QUALIFICATIONS
Experience \& Education
- Bachelor’s degree in computer science, Engineering, or related technical field (or equivalent experience)
- 8\+ years of software engineering experience with focus on infrastructure, platform engineering, or MLOps
- 3\+ years of hands\-on experience with machine learning infrastructure and deployment at scale
- 2\+ years of experience working with Large Language Models and transformer architectures
Technical Skills \- Core Requirements
- Proficient in Python; strong skills in Go, Rust, or Java preferred
- Proven experience working with open source LLMs (Llama 2/3, Qwen, Mistral, Gemma, Code Llama, etc.)
- Proficient in Kubernetes including custom operators, helm charts, and GPU scheduling
- Deep expertise in Azure services (AKS, Azure ML, Container Registry, Storage, Networking)
- Experience implementing and operating feature stores (Chronon, Feast, Tecton, Azure ML Feature Store, or custom solutions)
- Hands\-on experience with inference optimization using vLLM, TensorRT\-LLM, Triton Inference Server, or similar
DevOps \& Platform Skills
- Advanced experience with Azure DevOps, GitHub Actions, Jenkins, or similar CI/CD platforms
- Proficiency with Terraform, ARM templates, Pulumi, or CloudFormation
- Deep understanding of Docker, container optimization, and multi\-stage builds
- Experience with Prometheus, Grafana, ELK stack, Azure Monitor, and distributed tracing
- Knowledge of both SQL and NoSQL databases, data warehousing, and vector databases
Leadership \& Soft Skills
- Demonstrated track record of mentoring engineers and leading technical initiatives
- Experience leading design reviews with focus on compliance, performance, and reliability
- Excellent ability to explain complex technical concepts to diverse audiences
- Strong analytical and troubleshooting skills for complex distributed systems
- Experience managing cross\-functional technical projects and coordinating with multiple stakeholders
PREFERRED QUALIFICATIONS
Advanced Experience
- Master’s degree in computer science, Machine Learning, or related field
- 8\+ years of platform engineering or infrastructure experience
- Experience with Staff Engineer or Tech Lead roles in ML/AI organizations
- Background in distributed systems and high\-performance computing
- Open\-source contributions to ML infrastructure projects or LLM frameworks
Specialized Skills
- Multi\-Cloud Experience: Hands\-on experience with Azure, AWS (SageMaker, EKS) and/or GCP (Vertex AI, GKE)
- Experience with specialized hardware (A100s, H100s, TPUs, TEEs) and optimization
- RLHF \& Fine\-tuning: Experience with Reinforcement Learning from Human Feedback and LLM fine\-tuning workflows
- Experience with Milvus, Pinecone, Weaviate, Qdrant, or similar vector storage solutions
- Deep experience with MLflow, Kubeflow, DataRobot, or similar platforms
Industry Knowledge
- Understanding of AI safety principles, model governance, and regulatory compliance
- Background in regulated industries with understanding of data privacy requirements
- Experience supporting ML research teams and academic partnerships
- Deep understanding of GPU optimization, memory management, and high\-throughput systems
Hybrid\- (2 days a week)
Annual Salary
$115,000\.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.
At this time, GEICO will not sponsor a new applicant for employment authorization for this position. The GEICO Pledge:
Great Company: At GEICO, we help our customers through life’s twists and turns. Our mission is to protect people when they need it most and we’re constantly evolving to stay ahead of their needs.
We’re an iconic brand that thrives on innovation, exceeding our customers’ expectations and enabling our collective success. From day one, you’ll take on exciting challenges that help you grow and collaborate with dynamic teams who want to make a positive impact on people’s lives.
Great Careers: We offer a career where you can learn, grow, and thrive through personalized development programs, created with your career – and your potential – in mind. You’ll have access to industry leading training, certification assistance, career mentorship and coaching with supportive leaders at all levels.
Great Culture: We foster an inclusive culture of shared success, rooted in integrity, a bias for action and a winning mindset. Grounded by our core values, we have an an established culture of caring, inclusion, and belonging, that values different perspectives. Our teams are led by dynamic, multi\-faceted teams led by supportive leaders, driven by performance excellence and unified under a shared purpose.
As part of our culture, we also offer employee engagement and recognition programs that reward the positive impact our work makes on the lives of our customers.
Great Rewards: We offer compensation and benefits built to enhance your physical well\-being, mental and emotional health and financial future.
- Comprehensive Total Rewards program that offers personalized coverage tailor\-made for you and your family’s overall well\-being.
- Financial benefits including market\-competitive compensation; a 401K savings plan vested from day one that offers a 6% match; performance and recognition\-based incentives; and tuition assistance.
- Access to additional benefits like mental healthcare as well as fertility and adoption assistance.
- Supports flexibility\- We provide workplace flexibility as well as our GEICO Flex program, which offers the ability to work from anywhere in the US for up to four weeks per year.
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-$300K range is above the median for AI Software Engineer roles in our dataset (median: $189K across 518 roles with salary data).
Role Details
About This Role
AI Software Engineers build the applications and systems that AI models run inside. They own the API layers, data pipelines, frontend integrations, and infrastructure that turn a model into a product users interact with. Every AI company needs engineers who can build the software around the AI.
The challenge is building reliable systems around inherently unreliable components. Models are probabilistic. They'll give different answers to the same question. They hallucinate. They're slow. They're expensive. Your job is to build an application layer that handles all of this gracefully while delivering a product that users trust and enjoy.
Across the 26,159 AI roles we're tracking, AI Software Engineer positions make up 2% of the market. At GEICO, this role fits into their broader AI and engineering organization.
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
What the Work Looks Like
A typical week includes: building API endpoints that serve model inference with caching and fallback logic, designing the data pipeline that feeds context to a RAG system, implementing streaming responses in the frontend, debugging a race condition in the async inference pipeline, and optimizing database queries for the vector search layer. It's full-stack engineering with AI at the center.
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
Skills Required
Full-stack engineering skills with AI integration experience. Python and TypeScript are the most common requirements. You'll need to understand API design, database architecture, and how to build reliable systems around probabilistic outputs. Experience with streaming, async processing, and caching patterns is increasingly important as real-time AI applications proliferate.
Knowledge of vector databases, embedding APIs, and LLM integration patterns (function calling, structured outputs, retry logic) differentiates AI software engineers from general software engineers. Understanding cost optimization (caching strategies, model routing, batched inference) is valuable since inference costs can dominate application economics.
Strong postings describe the product you'll be building, the AI integration patterns you'll work with, and the scale requirements. Look for companies that have existing AI features and need engineers to improve and expand them, not companies that are 'planning to add AI' someday.
Compensation Benchmarks
AI Software Engineer roles pay a median of $235,100 based on 665 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($207K) sits 12% below the category median. Disclosed range: $115K to $300K.
Across all AI roles, the market median is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Architect ($292,900). By seniority level: Entry: $76,880; Mid: $131,300; Senior: $227,400; Director: $244,288; VP: $234,620.
GEICO AI Hiring
GEICO has 86 open AI roles right now. They're hiring across AI/ML Engineer, AI Agent Developer, AI Software Engineer. Positions span Bethesda, MD, US, Palo Alto, CA, US, Seattle, WA, US. Compensation range: $157K - $494K.
Location Context
Across all AI roles, 7% (1,863 positions) offer remote work, while 24,200 require on-site attendance. Top AI hiring metros: Los Angeles (1,695 roles, $178,000 median); New York (1,670 roles, $200,000 median); San Francisco (1,059 roles, $244,000 median).
Career Path
Common paths into AI Software Engineer roles include Software Engineer, Full-Stack Developer, Backend Engineer.
From here, career progression typically leads toward Staff Engineer, AI Architect, Engineering Manager.
If you're a software engineer, you're already 80% there. Learn the AI integration patterns: RAG, streaming inference, function calling, structured outputs. Build a project that demonstrates you can wrap an AI model in a production-quality application with proper error handling, caching, and user experience. That's the portfolio piece that gets you hired.
What to Expect in Interviews
Technical screens look like standard software engineering interviews with an AI twist. Expect system design questions about building reliable applications around probabilistic models: handling streaming responses, implementing retry logic for API failures, and designing caching strategies for LLM outputs. Coding rounds test standard algorithms plus practical integration patterns like async processing and rate limiting.
When evaluating opportunities: Strong postings describe the product you'll be building, the AI integration patterns you'll work with, and the scale requirements. Look for companies that have existing AI features and need engineers to improve and expand them, not companies that are 'planning to add AI' someday.
AI Hiring Overview
The AI job market has 26,159 open positions tracked in our dataset. By seniority: 2,416 entry-level, 16,247 mid-level, 5,153 senior, and 2,343 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (1,863 positions). The remaining 24,200 roles require on-site or hybrid attendance.
The market median for AI roles is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. Highest-paying categories: AI Engineering Manager ($293,500 median, 28 roles); AI Architect ($292,900 median, 108 roles); AI Safety ($274,200 median, 19 roles).
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
The AI Job Market Today
The AI job market spans 26,159 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (23,752), AI Software Engineer (598), AI Product Manager (594). 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 (2,416) are outnumbered by mid-level (16,247) and senior (5,153) 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 2,343 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (1,863 positions), with 24,200 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 $184,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $309,400. 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 Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $122,200. 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: Rag (16,749 postings), Aws (8,932 postings), Rust (7,660 postings), Python (3,815 postings), Azure (2,678 postings), Gcp (2,247 postings), Prompt Engineering (1,469 postings), Openai (1,269 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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