Interested in this AI/ML Engineer role at CarParts.com?
Apply Now →Skills & Technologies
About This Role
ABOUT US
We are a fast\-growing technology company building products used by hundreds of thousands of customers, backed by a supply chain spanning multiple distribution centers and a data platform processing millions of events daily. Engineering is not a support function here — it is a core driver of every major business outcome.
We move fast, hold ourselves to a high bar, and believe the best engineering decisions are made by people who are close to the business. We are embracing AI not as a trend, but as a genuine multiplier — using it to ship better software, faster, while never losing sight of the craftsmanship that makes software great. THE ROLE
This is a forward\-deployed engineering role with AI at its core. You will architect systems, write production\-grade code, design databases, and own your projects end to end — with a primary focus on deploying AI capabilities directly into the business and products that serve real users. What defines this role is the expectation that you bring strong engineering fundamentals together with a hands\-on, deployment\-first mindset for AI\-powered tools and workflows.
You are not required to be an AI researcher or an ML specialist. You are expected to be an excellent engineer who deploys AI where it creates real value — using it to accelerate delivery, build intelligent features, and solve hard problems, while applying your own judgment to validate, refine, and own the outcome. Strong Engineering Core* Design robust, scalable systems from the ground up
- Write clean, well\-tested, maintainable code
- Optimize database performance and data models
- Debug complex issues across the full stack
- Own code quality through rigorous peer review
- Deliver reliable software with measurable outcomes
AI as a Force Multiplier* Use AI coding assistants to accelerate development
- Leverage LLMs to generate boilerplate, tests, and docs
- Build AI\-powered features where they add real user value
- Apply AI to improve code review, debugging, and analysis
- Evaluate AI outputs critically — judgment still wins
- Stay current and bring new AI tools to the team
OUR CULTUREWhat We Believe* Outcomes over activity — we measure what ships and what works.
- Speed is a feature — long approval chains kill great products.
- Radical candor — honest feedback is a form of respect.
- Learning is non\-negotiable — every sprint is a chance to improve.
- No politics, no silos — collaborate openly across every team.
How We Work* Fast\-paced sprints with a strong bias toward shipping.
- Engineers own requirements, architecture, and roadmap input.
- AI tools are standard kit — we share what works.
- Blameless post\-mortems — failure is a learning event.
- Async\-first with intentional synchronous collaboration.
KEY RESPONSIBILITIESCore Software Engineering (50%)* Design, build, and maintain scalable, high\-quality software systems and APIs that serve real users in production.
- Write clean, well\-structured code with appropriate test coverage — unit, integration, and end\-to\-end.
- Architect and optimize relational and non\-relational database schemas, queries, and data models for performance and reliability.
- Conduct meaningful code reviews that improve team quality and share knowledge, not just catch syntax errors.
- Debug, profile, and resolve performance bottlenecks and production issues with urgency and rigor.
- Contribute to technical architecture decisions — propose solutions, evaluate tradeoffs, and document outcomes.
- Participate actively in Agile ceremonies: sprint planning, standups, retrospectives, and backlog refinement.
AI\-Forward Deployment (35%)* Deploy AI solutions end\-to\-end — from identifying the right use case, to building and shipping LLM\-powered features directly into products and internal workflows.
- Incorporate LLM APIs and AI frameworks into product features where they create genuine user value: search, summarization, recommendations, intelligent automation, and decision support.
- Apply critical engineering judgment to evaluate, refine, and validate all AI\-generated outputs before they reach production — you own the result, not just the prompt.
- Use AI coding assistants (GitHub Copilot, Cursor, Claude Code) as a daily accelerator — and champion effective AI tool patterns and prompt strategies across the team.
- Stay at the front edge of the AI tooling landscape — evaluate new models, frameworks, and techniques and bring back deployment\-ready recommendations that move the business forward.
Cross\-Team Collaboration \& Communication (15%)* Partner with Marketing, Customer Experience, Data Science, Merchandising, Warehouse Ops, and Finance to understand requirements and deliver technical solutions.
- Translate technical concepts clearly for non\-technical stakeholders — written documentation, presentations, and live discussions.
- Present project outcomes and architectural decisions to senior leadership with confidence and clarity.
- Contribute to a culture of knowledge sharing: write internal documentation, run team demos, and mentor peers.
CROSS\-TEAM COLLABORATION
Engineering here is a visible, active partner across the business — not a back\-room function. You will work directly with teams who depend on the systems and data you build. Strong communication and commercial awareness are just as important as great code.* Marketing: Personalization, campaign analytics, A/B platforms
- Customer Experience: AI support tools, self\-service flows, CSAT pipelines
- Data Science: Model integration, feature engineering, shared infra
- Merchandising: Pricing, inventory intelligence, catalog tooling
- Warehouse Ops: Fulfillment automation, routing, operational dashboards
- Finance \& Ops: Cost models, reporting pipelines, forecasting
- Supply Chain: Vendor integrations, PO systems, logistics optimization
- Product: Feature scoping, roadmap input, rapid prototyping
- Security: Secure design, data privacy, compliance tooling
REQUIRED QUALIFICATIONSEngineering Fundamentals — Non\-Negotiable* Experience: 3–6 years of professional software engineering in a production environment, with a portfolio of real systems you have owned and shipped.
- Languages: Strong proficiency in one or more of: Python, Java, TypeScript, Go, or C\#. Depth matters more than breadth.
- Software Design: Solid grasp of OOP, SOLID principles, design patterns, and how to make architectural decisions with long\-term maintainability in mind.
- Databases: Confident with relational databases (PostgreSQL, MySQL) — schema design, indexing, query optimization, and transactions. Working knowledge of at least one NoSQL store (MongoDB, Redis, DynamoDB).
- APIs \& Integration: Experience designing and consuming RESTful APIs; comfortable reading and writing service contracts and integration documentation.
- Testing: Writes meaningful unit, integration, and end\-to\-end tests — not for coverage metrics, but for genuine confidence in your code.
- Cloud \& DevOps: Familiar with at least one major cloud platform (AWS, GCP, Azure), Docker, CI/CD pipelines, and basic infrastructure practices.
- Version Control: Strong Git workflow: branching strategies, pull requests, and code review culture.
AI Literacy — Expected \& Growing* AI Tool Adoption: Actively uses AI coding assistants in day\-to\-day development and can demonstrate concrete productivity or quality improvements as a result.
- LLM Integration: Has built or integrated at least one LLM\-powered feature or workflow in a real project — even if exploratory or side\-project experience counts.
- Prompt Awareness: Understands the basics of prompt design, few\-shot examples, and how to get reliable, structured outputs from LLM APIs.
- Critical Evaluation: Applies engineering discipline to AI outputs — tests them, validates them, and knows when not to trust them.
- Curiosity: Genuinely interested in how AI tooling is evolving and proactively experiments with new approaches.
PREFERRED QUALIFICATIONS* Experience building RAG pipelines or working with vector databases (Pinecone, pgvector, Weaviate, etc.).
- Familiarity with AI frameworks such as LangChain, LlamaIndex, or similar orchestration tools.
- Exposure to ML concepts: embeddings, model evaluation, fine\-tuning, and working with data science teams.
- Experience with observability and monitoring tooling (Datadog, Grafana, OpenTelemetry, or equivalent).
- Background in microservices architecture and event\-driven systems (Kafka, RabbitMQ, etc.).
- Knowledge of security best practices: OWASP Top 10, authentication/authorization, input validation.
- Experience with Agile/Scrum methodologies and tools like Jira or Linear.
- Open\-source contributions or public portfolio demonstrating your engineering work.
CORE COMPETENCIES* Engineering Craft: Clean, tested, maintainable code
- Database Acumen: SQL, NoSQL, schema \& performance
- AI Fluency: Tools \+ LLMs as force multipliers
- Performance Mindset: Measures, optimizes, validates
- Quality Discipline: Tests like production depends on it
- Cross\-Team Voice: Fluent in code AND in business
- Sound Judgment: Knows when — and when NOT — to use AI
- Ownership Drive: Ships end\-to\-end, measures impact
TECHNOLOGY LANDSCAPELanguages: Python, TypeScript, Java, Go, SQLDatabases: PostgreSQL, MySQL, MongoDB, Redis, DynamoDB, pgvectorCloud \& Infra: AWS / GCP / Azure, Docker, Kubernetes, TerraformAPIs: REST, GraphQL, gRPC, OpenAPI / SwaggerAI \& LLM: OpenAI, Anthropic Claude, Gemini — integrated as features, not the foundationAI Dev Tools: GitHub Copilot, Cursor, Claude Code — used daily to accelerate engineeringObservability: Datadog, OpenTelemetry, Prometheus, GrafanaData: PostgreSQL, dbt, Airflow, Spark, KafkaCI/CD: GitHub Actions, ArgoCD, Jenkins, CircleCI 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.*
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 3,824 AI roles we're tracking, AI/ML Engineer positions make up 71% 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 $178,940 based on 11,900 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $160,000.
Across all AI roles, the market median is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $97,380; Mid: $160,000; Senior: $227,400; Director: $243,000; VP: $250,000.
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: $200K - $200K.
Location Context
Across all AI roles, 16% (613 positions) offer remote work, while 3,187 require on-site attendance. Top AI hiring metros: New York (2,448 roles, $210,000 median); San Francisco (1,990 roles, $253,000 median); Los Angeles (1,686 roles, $189,000 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 3,824 open positions tracked in our dataset. By seniority: 119 entry-level, 1,813 mid-level, 1,472 senior, and 420 leadership roles (Director, VP, C-Level). Remote roles make up 16% of the market (613 positions). The remaining 3,187 roles require on-site or hybrid attendance.
The market median for AI roles is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($293,500 median, 31 roles); AI Safety ($274,200 median, 51 roles); Research Engineer ($260,000 median, 401 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 3,824 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,702), Data Scientist (281), AI Software Engineer (258). 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 (119) are outnumbered by mid-level (1,813) and senior (1,472) 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 420 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 16% of all AI roles (613 positions), with 3,187 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 $200,000. Top-quartile roles start at $253,000, 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 $293,500 median, while Prompt Engineer roles sit at $142,800. 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 (1,968 postings), Aws (1,203 postings), Azure (882 postings), Rag (877 postings), Gcp (735 postings), Prompt Engineering (587 postings), Pytorch (586 postings), Claude (554 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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.