This role is full-time, in-office at our Long Beach, CA headquarters. We're not able to offer remote or hybrid arrangements, so please only apply if you're able to work onsite full-time.
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 Culture
What 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 Responsibilities
Core 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 Qualifications
Engineering 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 Landscape
Languages: Python, TypeScript, Java, Go, SQL
Databases: PostgreSQL, MySQL, MongoDB, Redis, DynamoDB, pgvector
Cloud & Infra: AWS / GCP / Azure, Docker, Kubernetes, Terraform
APIs: REST, GraphQL, gRPC, OpenAPI / Swagger
AI & LLM: OpenAI, Anthropic Claude, Gemini — integrated as features, not the foundation
AI Dev Tools: GitHub Copilot, Cursor, Claude Code — used daily to accelerate engineering
Observability: Datadog, OpenTelemetry, Prometheus, Grafana
Data: PostgreSQL, dbt, Airflow, Spark, Kafka
CI/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.
Skills Required
- 3-6 years of professional software engineering experience in a production environment
- Strong proficiency in Python, Java, TypeScript, Go, or C#
- Knowledge of object-oriented programming, SOLID principles, design patterns, and maintainable architecture
- Experience with relational databases such as PostgreSQL or MySQL, including schema design, indexing, query optimization, and transactions
- Working knowledge of at least one NoSQL database such as MongoDB, Redis, or DynamoDB
- Experience designing and consuming RESTful APIs
- Ability to write unit, integration, and end-to-end tests
- Familiarity with AWS, GCP, or Azure
- Familiarity with Docker, CI/CD pipelines, and basic infrastructure practices
- Strong Git workflow experience, including branching, pull requests, and code reviews
- Experience using AI coding assistants in day-to-day development
- Experience building or integrating an LLM-powered feature or workflow
- Understanding of prompt design, few-shot examples, and structured LLM outputs
- Ability to test, validate, and critically evaluate AI-generated outputs
- Interest in experimenting with evolving AI tools and approaches
- Experience building RAG pipelines or using vector databases such as Pinecone, pgvector, or Weaviate
- Familiarity with AI frameworks such as LangChain or LlamaIndex
- Exposure to embeddings, model evaluation, fine-tuning, and collaboration with data science teams
- Experience with Datadog, Grafana, OpenTelemetry, or equivalent observability tools
- Experience with microservices and event-driven systems such as Kafka or RabbitMQ
- Knowledge of OWASP Top 10, authentication, authorization, and input validation
- Experience with Agile/Scrum and Jira or Linear
- Open-source contributions or a public engineering portfolio
What We Do
Regardless of your experience level with cars, we believe that getting the right parts for yours should be easy, so you can reinvest your time and energy in the journey ahead. Which is why at CarParts.com, we put the customer experience first. With a simple and convenient online platform, we have transformed the way drivers shop for parts in today’s digital world. It’s smarter, quicker, and hassle-free. With over 25 years of experience, and 50 million parts delivered, we’ve streamlined our website and sourcing network to better serve the way drivers get the parts they need. Utilizing the latest technologies and design principles, we’ve created an easy-to-use, mobile-friendly shopping experience that, alongside our own nationwide distribution network, cuts out the brick-and-mortar supply chain costs and provides quality parts at a budget-friendly price. At CarParts.com, we know that drivers need to get back on the road quickly, so no matter who you are, what you drive, or where you’re going, we’re here for you.
