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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
We are investing heavily in AI\-powered engineering — building intelligent agents, integrating LLMs into our platform, and developing MCP (Model Context Protocol) servers to orchestrate context\-aware automation across our ecommerce stack. This role sits at the intersection of full\-stack engineering and applied AI.
Why This Role Is AI\-Forward
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
- 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 tabztrace 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
Build \& Ship Product Features (50%)
- 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:ReActloops, 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, andcostguardrails
- Write prompts, evaluation harnesses, and monitoring for AI features — treat them as code, version them, review them
AI \& Automation Requirements \& Developer Experience (30%)
- Optimize frontend performance: Core Web Vitals, page load time, time\-to\-first\-byte
- Design APIs (REST,OpenAPI) that are clean, well\-documented, andbackward\-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%)
- 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
- Contributions to open\-source AI tooling or MCP ecosystem
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.
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.
Position Requirements
Core Engineering (Required)
- 5\+ years of experience in full\-stack web application development using Node.js, JavaScript, TypeScript, and modern frameworks
- Extensive experience building scalable applications and microservices using React, Next.js, Node.js, Express, HTML, and CSS
- Hands\-on TypeScript across frontend and backend systems
- Strong knowledge of RESTful API design andOpenAPIspecifications
- Experience designing and integrating APIs, including REST and modern data\-fetching patterns
- Extensive experience with MySQL, MongoDB, PostgreSQL, and Redis, with 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 (Redux or equivalent)
- Experience with cloud\-native development using Docker and containerized environments
- Experience with CDNs, caching strategies, performance optimization, and security considerations
- Strong knowledge of JavaScript build tools (Webpack, Vite, or modern bundlers)
- Proficiency with Chrome DevTools and frontend performance profiling
- Experience with SPA, PWA, responsive design, and MPA architectures
- Strong foundation in data structures, algorithms, and database design
- Proven experience in software architecture, design patterns, and engineering best practices
AI \& Automation (Required)
- 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
- Solid understanding of agentic concepts and design patterns:
- ReAct(Reason \+ Act) loops, chain\-of\-thought planning, and step\-by\-step task decomposition
- Tool orchestration — selecting, invoking, and chaining external tools based on model reasoning
- Memory architectures: conversation context, scratchpads, vector\-backed long\-term recall
- Self\-correction and reflection — agents that detect errors in their own output and retry
- Human\-in\-the\-loop checkpoints, confidence thresholds, and graceful fallback to manual workflows
- Multi\-agent coordination — delegating subtasks across specialized agents and merging results
- Acquaintance or hands\-on experience developing agents:
- Built, extended, or shipped at least one agent (production, internal tool, or well\-scoped prototype) that performs multi\-step autonomous tasks
- Familiar with agent frameworks such asLangChain,LangGraph,CrewAI,Autogen, Claude Agent SDK, or custom orchestration loops
- Comfortable designing agent tool schemas, managing agent state, and debugging non\-deterministic agent behavior
- Experience designing or contributing to MCP servers or similar context\-orchestration layers
- Proven approach to token budget management: prompt optimization, caching strategies, and cost monitoring
- Comfortable using AI coding assistants (GitHub Copilot, Claude Code, Claude Cowork, Cursor) daily to accelerate development
- Able to write effective prompts for code generation, refactoring, test creation, and documentation
- Understands foundational LLM concepts: tokens, temperature, context windows, embeddings, and RAG
- Can evaluate AI\-generated code for correctness, security, and performance — not just accept output blindly
Nice to Have
- Experience with public cloud services (AWS, Azure, GCP)
- Experience with ecommerce/retail purchase journeys
- Experience migrating legacy applications to modern stacks
- Familiarity with vector databases (Pinecone,Weaviate,pgvector) and RAG pipelines
- Experience with agent frameworks (LangChain,LangGraph,CrewAI) or custom orchestration loops
- Experience fine\-tuning or distilling models for domain\-specific tasks
- Contributions to open\-source AI tooling or the MCP ecosystem
- Experience withGraphQL
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.
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 $156K-$219K range is above the median 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 CarParts.com, 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. Mid-level AI roles across all categories have a median of $194,400. This role's midpoint ($187K) sits 13% below the category median. Disclosed range: $156K to $219K.
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.
CarParts.com AI Hiring
CarParts.com has 2 open AI roles right now. They're hiring across AI Software Engineer, AI/ML Engineer. Based in Long Beach, CA, US. Compensation range: $219K - $219K.
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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