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About This Role
AI Engineer CURE Auto Insurance is a leading direct writer of auto insurance in New Jersey, Pennsylvania, and Michigan. CURE offers a great working environment, competitive salary, and comprehensive benefits package which includes health and dental coverage, life and disability insurance, 401k with generous company match, and much more!
We are seeking a skilled and motivated AI Engineer to design, develop, and deploy AI-powered applications and automation solutions. The ideal candidate will have hands-on experience with Generative AI, Large Language Models (LLMs), cloud technologies, and integrating AI capabilities into enterprise applications. Key Responsibilities* Design, develop, test, and deploy AI/ML solutions and intelligent automation applications.
- Build and integrate Generative AI solutions using LLMs, APIs, and AI frameworks.
- Develop AI agents, Retrieval-Augmented Generation (RAG) pipelines, and document processing solutions.
- Build scalable backend services and REST APIs to integrate AI capabilities with existing systems.
- Work with structured and unstructured data, including document extraction, OCR, and automated analysis.
- Deploy, monitor, and optimize AI applications in cloud environments.
- Collaborate with cross-functional teams to translate business requirements into technical solutions.
- Ensure AI solutions meet performance, security, reliability, and data privacy standards.
Required Qualifications* Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Data Science, or a related field.
- 5+ years of software development experience in Java or .net or python, in that 2 years hands-on AI/ML development.
- Strong proficiency in Python, SQL, and API development.
- Experience working with LLMs, Generative AI, and prompt engineering.
- Familiarity with AI frameworks such as LangChain, LangGraph, or similar technologies.
- Experience with cloud platforms such as Google Cloud Platform (GCP), AWS, or Azure.
- Knowledge of REST APIs, databases, Git, Docker, and CI/CD pipelines.
- Strong analytical, debugging, and problem-solving skills.
- Preferred Qualifications
- Experience with Google Vertex AI, Gemini, OpenAI, or other enterprise AI platforms.
- Experience with agentic AI, RAG architectures, embeddings, and vector databases.
- Familiarity with Google Cloud Run, Kubernetes, and cloud-native application development.
- Experience with Document AI, OCR, and intelligent document processing.
- Understanding of MLOps, AI model evaluation, monitoring, and optimization.
- Familiarity with enterprise security, responsible AI, and AI governance principles
Benefits* Comprehensive health benefits including medical, dental and vision coverage
- Generous paid time off (PTO days, sick days, and holidays)
- Flexible spending options with FSA & HSA plans
- Life and AD&D insurance
- 401(k) with company match
- Voluntary benefits such as legal services, pet insurance, and identity & fraud protection
- 24/7 Employee Assistance Program (EAP) for employees and their families
- Health & wellness perks including gym discounts, wellness reimbursements, and reward programs
- Fun, engaging in-office events that support our culture
Physical Actions/Environment: Required job duties consist of prompt and regular attendance, ability to frequently move about the office to coordinate work with others; standing, sitting and typing for extended periods; and lifting and/or carrying up to 5 lbs. Ability to frequently communicate with others in-person, on the phone/virtually, and in writing. Ability to read, understand, process and evaluate large amounts of technical information and make related, informed decisions. Starting Salary: $100,000 - $180,000 annually based on experience Schedule: We offer flexible work hours between 8:00 AM and 5:00 PM, and employees can choose a schedule that works best for them—8:00 AM–4:00 PM, 8:30 AM–4:30 PM, or 9:00 AM–5:00 PM. While we aim to support individual preferences, final schedules may be adjusted based on business needs to ensure great coverage and teamwork.
We offer a hybrid work schedule: team members work onsite 4 days per week and have the flexibility to work remotely 1 day per week. Location: Southfield, MI or Princeton, NJ
We recruit, hire, employ, train and promote, and compensate individuals based on job-related qualifications and abilities. We respect the dignity and worth of each individual and are committed to an employment environment that is free from all forms of employment discrimination.
CURE Auto Insurance provides equal employment opportunities to all employees and applicants for employment and prohibits discrimination and harassment of any type without regard to race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, pregnancy, gender identity or expression, or any other characteristic protected by federal, state or local laws.
This policy applies to all terms and conditions of employment, including recruiting, hiring, placement, promotion, termination, layoff, recall, transfer, leaves of absence, compensation and training. Disclaimer: This job description reflects management's assignment of essential functions; it does not prescribe or restrict the tasks that may be assigned. This job description may be subject to change at any time. Applicants must be authorized to work for ANY employer in the U.S. We are unable to sponsor or take over sponsorship of an employment Visa at this time.**
Salary Context
This $100K-$180K range is below the median for AI/ML Engineer roles in our dataset (median: $178K across 2239 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 11,520 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At CURE Auto Insurance, 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 $212,040 based on 13,780 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($140K) sits 34% below the category median. Disclosed range: $100K to $180K.
Across all AI roles, the market median is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($266,050) and AI Engineering Manager ($253,461). By seniority level: Entry: $112,320; Mid: $193,200; Senior: $230,000; Director: $272,250; VP: $235,830.
CURE Auto Insurance AI Hiring
CURE Auto Insurance has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Princeton, NJ, US. Compensation range: $180K - $180K.
Location Context
Across all AI roles, 6% (708 positions) offer remote work, while 10,737 require on-site attendance. Top AI hiring metros: New York (3,257 roles, $221,000 median); San Francisco (2,766 roles, $260,550 median); Los Angeles (1,580 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 11,520 open positions tracked in our dataset. By seniority: 140 entry-level, 5,630 mid-level, 4,176 senior, and 1,574 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (708 positions). The remaining 10,737 roles require on-site or hybrid attendance.
The market median for AI roles is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($266,050 median, 70 roles); AI Engineering Manager ($253,461 median, 58 roles); Research Engineer ($252,000 median, 476 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 11,520 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (8,769), AI Software Engineer (586), AI Product Manager (511). 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 (140) are outnumbered by mid-level (5,630) and senior (4,176) 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 1,574 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (708 positions), with 10,737 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 $213,480. Top-quartile roles start at $264,000, 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 $266,050 median, while Prompt Engineer roles sit at $140,000. 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 (2,267 postings), Aws (1,178 postings), Azure (972 postings), Rag (924 postings), Gcp (715 postings), Pytorch (586 postings), Claude (572 postings), Kubernetes (559 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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