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About This Role
What We Do
CarParts.com is the go\-to eCommerce platform for auto care and maintenance. We provide drivers with quality parts at competitive prices and enable them to schedule appointments with trusted mechanics directly through our website. Using world\-class design principles and the latest technologies, we deliver a fast, intuitive digital experience backed by our company\-owned national distribution network.
With over 1,000 employees worldwide, we are scaling rapidly, fueled by our most recent strategic partnership and $35 million investment. This positions us for the next phase of growth as we continue to empower drivers along their journey.
Our Culture
At CarParts.com, our culture goes beyond our core values of Safety First, Customer Focused, and Commitment to Excellence. We are a performance\-driven, data\-focused, and fast\-paced team where results matter and winning is expected.
- Hungry \& Hardworking: We set ambitious goals, measure progress with clear metrics, and hold ourselves accountable to deliver results.
- Promote from Within: We reward top performers with opportunities for growth and advancement.
- Collaborative \& In\-Person: We believe the best ideas and fastest execution happen face\-to\-face.
\- High Standards: We move quickly, pay attention to details, and dig deep \- whether it’s analyzing contracts, aggregating complex scenarios, or building clear, data\-driven presentations.
- No Passengers: We value grit, ownership, and the relentless pursuit of results
Why This Role Is AI\-Forward
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This is not a traditional full\-stack role with AI bolted on. AI is woven into how we build, ship, and operate:
- Every engineer uses AI coding assistants (Claude Code, Claude Cowork, GitHub Copilot, Cursor) as a daily multiplier — not an optional extra
- We are building production AI agents that automate merchandising, search relevance, customer support triage, and content generation
- Our agents use agentic patterns — autonomous planning, multi\-step reasoning, tool orchestration, self\-correction, and human\-in\-the\-loop checkpoints — not simple prompt\-response chains
- Our platform exposes MCP servers so LLM\-powered tools can read catalog data, trigger workflows, and act on real\-time signals
- We treat prompt engineering and token economics as first\-class engineering disciplines, reviewed in PRs alongside application code
If you want to ship AI features that millions of customers interact with — not just prototype in a notebook — this is the role.
Who You Are
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- The Builder: You’d rather write a reusable abstraction, a CLI tool, or a code generator than repeat the same manual task twice — and you design systems that scale without you babysitting them.
- The Troubleshooter: A user reports a broken checkout flow and you instinctively open the network tabz trace the API call, and pinpoint whether it’s a frontend state bug, a backend validation edge case, or a data mismatch — before anyone else finishes reading the ticket.
- The AI Enthusiast: You treat token budgets and prompt design with the same rigor as component architecture — optimizing context windows, evaluating model trade\-offs, and shipping AI\-powered features that move product metrics.
What You’ll Do
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Build \& Ship Product Features (50%)
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- Design, develop, and own full\-stack features across React/Next.js frontends and Node.js/Express microservices
- Build AI\-powered product experiences: intelligent search, personalized recommendations, automated content generation, and conversational commerce flows
- Design and develop agentic systems — agents that plan, reason over multiple steps, select and call tools, handle errors autonomously, and escalate to humans when confidence is low
- Implement agentic patterns: ReAct loops, chain\-of\-thought planning, reflection/self\-critique, memory (short\-term context and long\-term retrieval), and multi\-agent coordination
- Develop and maintain MCP servers that expose ecommerce domain tools (catalog, pricing, inventory, order) to LLM\-powered clients
- Integrate LLM APIs into production paths with proper error handling, fallback strategies, and cost guardrails
- Write prompts, evaluation harnesses, and monitoring for AI features — treat them as code, version them, review them
AI \& Automation Requirements \& Developer Experience (30%)
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- Optimize frontend performance: Core Web Vitals, page load time, time\-to\-first\-byte
- Design APIs (REST, OpenAPI) that are clean, well\-documented, and backward\-compatible
- Build shared tooling: CLI utilities, code generators, reusable component libraries, and internal developer tools powered by AI
- Improve CI/CD pipelines, containerized builds, and deployment workflows
- Participate in architecture decisions, code reviews, and technical design documents
- 1\+ year hands\-on experience integrating LLM APIs (OpenAI, Anthropic, or equivalent) into production or near\-production systems
- Demonstrated ability to build or extend AI agents that use tool\-calling, function execution, and structured output
- Proven approach to token budget management: prompt optimization, caching strategies, cost monitoring dashboards
Collaborate \& Grow (20%)
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- Translate business requirements into technical designs with product and design stakeholders
- Mentor engineers on AI integration patterns, prompt engineering, and modern full\-stack practices
- Stay current with AI/ML tooling, LLM advances, and MCP ecosystem developments — bring what you learn back to the team
Position Requirements
Core Engineering
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- 6\+ years of experience in Full\-Stack JavaScript and TypeScript development
- Extensive experience building scalable full\-stack applications and microservices using React, Next.js, Node.js, Express, HTML, and CSS
- Hands\-on experience with TypeScript across frontend and backend systems
- Strong knowledge of RESTful API design and OpenAPI specifications
- Experience designing and integrating APIs, including REST and modern data\-fetching patterns
- Extensive experience working with MySQL, MongoDB, PostgreSQL and Redis, with a solid understanding of data modeling trade\-offs
- Familiarity with micro\-frontend architecture and module federation
- Strong experience building performant React applications using hooks and state management libraries such as Redux
- Experience with cloud\-native application development using Docker and containerized environments
- Experience with CDNs, caching strategies, performance optimization, and security considerations
- Strong knowledge of JavaScript build tools such as Webpack and modern bundlers
- Proficiency with Chrome DevTools and frontend performance profiling tools
- Experience with SPA, PWA, responsive design, and MPA architectures, including performance
- Strong experience with data structures, algorithms, and database design
- Proven experience in software architecture, design patterns, and engineering best practices
Nice to Have
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- Experience with public cloud services (AWS, Azure and GCP)
- Experience with ecommerce/retail
- Experience with legacy applications migration to modern stack
- Contributions to open\-source AI tooling or MCP ecosystem
Equal Opportunity Employer
*CarParts.com is an equal\-opportunity employer. We enthusiastically accept our responsibility to make employment decisions without regard to race, religious creed, color, age, sex, sexual orientation, national origin, religion, marital status, medical condition, physical or mental disability, military service, pregnancy, childbirth and related medical conditions, or any other classification protected by federal, state, and local laws and ordinances. Our management is dedicated to ensuring that we fulfill this policy with respect to hiring, placement, promotion, transfer, demotion, layoff, termination, recruitment advertising, pay, and other forms of compensation, training, and general treatment during employment.*
*The above\-noted job description is not intended to describe, in detail, the multitude of tasks that may be assigned but rather to give the incumbent a general sense of the responsibilities and expectations of his/her position. As the nature of business demands change so, too, may the essential functions of this position.*
Salary Context
This $146K-$200K range is below the median for AI Software Engineer roles in our dataset (median: $190K across 219 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 3,823 AI roles we're tracking, AI Software Engineer positions make up 7% of the market. At CarParts.com, 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 $232,000 based on 797 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($173K) sits 25% below the category median. Disclosed range: $146K to $200K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
CarParts.com AI Hiring
CarParts.com has 1 open AI role right now. They're hiring across AI Software Engineer. Based in Long Beach, CA, US. Compensation range: $200K - $200K.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, 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 $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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