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About This Role
We are seeking a Senior Developer to join a dedicated Good Sam AI team focused exclusively on building new applications at extreme speed, using AI tools like Claude to handle the majority of hands\-on code generation. This role blends strong engineering fundamentals with AI orchestration skill: instead of writing every line by hand, you direct, prompt, review, and harden AI\-generated code so it ships fast without sacrificing quality. You will work shoulder\-to\-shoulder with a small, tightly\-scoped squad whose sole mission is shipping new applications in days rather than weeks.
Key Responsibilities:
- Use AI coding tools such as Claude and Claude Code as the primary means of writing, refactoring, and testing code across the full stack, rather than hand\-coding every line.
- Translate product requirements directly into precise prompts, specs, and AI agent instructions that produce production\-ready code.
- Rapidly prototype, build, and ship new applications within compressed War Room sprints, often measured in days rather than weeks.
- Review, validate, and harden AI\-generated code for correctness, security, performance, and maintainability before it ships.
- Orchestrate multi\-step and multi\-agent AI workflows to handle end\-to\-end feature builds — design, code, test, and deploy.
- Collaborate in real time with product owners, designers, and fellow War Room engineers to resolve ambiguity and keep AI\-driven development unblocked.
- Maintain architecture, coding, and integration standards even as delivery speed increases.
- Continuously evaluate emerging AI coding tools and models, adopting those that improve speed, quality, or reliability.
Requirements:
- Strong Full stack Development experience, leaning heavily on Back end services (Node, Java), sufficient to read, debug, and extend AI\-generated code with confidence.
- Proficiency in database technologies (SQL, MySQL, PostgreSQL, or Oracle).
- Solid understanding of integration patterns and design principles.
- Experience with GIT or other version\-control systems.
- Hands\-on experience directing AI coding assistants (e.g., Claude, Claude Code, GitHub Copilot, Cursor) to generate and modify production code.
- Demonstrated skill in prompt engineering and writing technical specs that AI tools can execute against reliably.
- Comfort operating at high speed in a compressed, iteration\-heavy delivery model with minimal ceremony.
Preferred Skills:
- Experience with Azure cloud services.
- Experience with Harness pipeline.
- Experience with agentic AI workflows or multi\-agent orchestration tools.
- Familiarity with Kubernetes (k8s) for container orchestration.
- Knowledge of deployment and caching strategies.
Mindset:
- Thrives in a fast, high\-pressure, build\-and\-iterate environment dedicated solely to new application development.
- Sound judgment on when to trust AI\-generated output versus when to intervene manually, with a clear understanding of the limits of AI\-generated code.
- Strong communication skills to bridge technical and business requirements in real time within a tightly collaborative team.
- Strategic thinker with a passion for innovation, continuously raising the bar on how fast and how well the team ships using AI.
General Compensation Disclosure
The pay range for this role considers several factors in making compensation decisions including but not limited to skill sets; experience and training; licensure and certifications; and other business and organizational needs. At Camping World, it is not typical for an individual to be hired at or near the top of the range for their role and compensation decisions are dependent on the factors stated. A reasonable estimate of the current range is listed below.
Pay Range:
$91,800\.00\-$140,700\.00 Annual
In addition to competitive pay, we offer Paid Time Off, 401(k), an Employee Assistance Program, Good Sam Roadside Assistance, discounts, paid parental leave (if eligibility is met), Tuition Reimbursement (if eligibility is met), and on the job training opportunities. Full\-time associates are offered a comprehensive benefit package including medical, dental, vision and more! Part\-time associates are offered access to dental \& vision coverage! For more information please visit: www.mycampingworldbenefits.com
We are an equal employment opportunity employer. The Company's policy is not to discriminate against any applicant or employee based on race, color, sex, sexual orientation, gender identity, religion, national origin, age (40 and over), disability, veteran or uniformed service\-member status, genetic information, or any other basis protected by applicable federal, state, or local laws.
Salary Context
This $91K-$140K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Camping World, 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 $214,900 based on 6,420 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($116K) sits 46% below the category median. Disclosed range: $91K to $140K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Camping World AI Hiring
Camping World has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in Lincolnshire, IL, US. Compensation range: $140K - $249K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 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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