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Entry Level Full Stack Developer Jobs in Irvine, CA

Infosys Limited Digital

Java Full Stack Developer

Irvine, CA · On-site

$56.25 - $72.75/hr

... DevOps|Continuous integration - Java Salary min 81102 Salary max 109353 Overview The Infosys Financial Services unit is a global leader in driving digital transformation for financial institutions.

Infosys

Java Full Stack Developer

Irvine, CA · On-site

$56.25 - $72.75/hr

In the assigned Job Role of Technology Consultant 2, your Area Of Responsibility will be as below: • Contribute to the requirements elicitation process by documenting assigned parts of business ...

Infosys

Java Full Stack Developer

Irvine, CA · On-site

$56.25 - $72.75/hr

In the assigned Job Role of Technology Consultant 2, your Area Of Responsibility will be as below: • Contribute to the requirements elicitation process by documenting assigned parts of business ...

Ask IT consulting

Full Stack .Net Developer

Irvine, CA · On-site

Key experience: l Database Analysis experience Full stack .NET developer with REST and JSON l Willingness to work with legacy systems written in VBA and Access l Strong communication skills with the ...

Anduril Industries

Software Engineer, Full Stack

Irvine, CA · On-site

$166K - $220K/yr

... engineering tradeoffs along the way. You'll work closely with product design and cross-functional teams to deliver full-stack solutions that align with and advance our mission. Your efforts will ...

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Entry Level Full Stack Developer information

See Irvine, CA salary details

$25

$62

$91

How much do entry level full stack developer jobs pay per hour?

As of Sep 7, 2026, the average hourly pay for entry level full stack developer in Irvine, CA is $62.86, according to ZipRecruiter salary data. Most workers in this role earn between $52.26 and $72.40 per hour, depending on experience, location, and employer.

What is an entry level full stack developer?

An Entry Level Full Stack Developer is a professional who is new to the software development field and works on both the front-end (client side) and back-end (server side) of web applications. They are familiar with various programming languages, frameworks, and tools needed to build and maintain entire web projects. While they may have limited experience, they are capable of handling tasks such as developing user interfaces, creating APIs, managing databases, and deploying applications under the guidance of more experienced developers. Entry level full stack developers often work as part of a team and receive mentorship to help them grow their skills.

What is the job of an entry level full stack developer?

A full stack is the front and back end of an application. It is comprised of a computer system, a programming language, database software, and a computer server. As an entry-level full stack developer, your responsibilities consist of developing something on behalf of a client. In an entry-level full stack developer role, you may help build an SQL database, a JavaScript application, or a PHP database on a server. Your qualifications should include a general knowledge of every level of software development as well as one or more common programming languages such as HTML, CSS, Python, and SQL.

What are the key skills and qualifications needed to thrive as an entry level full stack developer, and why are they important?

To thrive as an Entry Level Full Stack Developer, you need proficiency in both front-end (HTML, CSS, JavaScript) and back-end (e.g., Node.js, Python, or Java) technologies, supported by a relevant degree or coding bootcamp experience. Familiarity with databases (SQL/NoSQL), version control systems like Git, and frameworks such as React or Express is typically required. Strong problem-solving skills, attention to detail, and effective communication help you work collaboratively and adapt to changing project requirements. These skills and tools are vital for building, maintaining, and improving dynamic web applications in fast-paced development environments.

What are some common challenges entry level full stack developers face when transitioning from academic projects to real world applications?

Entry Level Full Stack Developers often find that real-world projects are more complex and less structured than academic assignments. They may encounter challenges such as working with legacy code, collaborating across multidisciplinary teams, and managing competing priorities within agile development cycles. Additionally, adapting to company-specific workflows, version control practices, and deployment processes can be initially overwhelming. However, these experiences provide valuable learning opportunities and quickly build practical, in-demand skills.

What is the difference between Entry Level Full Stack Developer vs Junior Web Developer?

AspectEntry Level Full Stack DeveloperJunior Web Developer
Required SkillsBasic knowledge of front-end and back-end technologies, programming languages like JavaScript, HTML, CSS, and some backend frameworksFundamental web development skills, mainly front-end or back-end, with limited full-stack experience
Work EnvironmentCollaborates on full project cycles, working on both client and server-side codeFocuses on specific parts of web development, often under supervision
Common UsageUsed in companies seeking versatile developers capable of handling full-stack tasksOften entry-level roles focusing on specific web development tasks

In summary, Entry Level Full Stack Developers have a broader skill set covering both front-end and back-end development, while Junior Web Developers typically specialize in one area with limited full-stack responsibilities. The choice depends on your desired focus and career path in web development.

More about Entry Level Full Stack Developer jobs

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For Entry Level Full Stack Developer jobs in Irvine, CA, the most frequently searched job titles are:

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The top searched job categories for Entry Level Full Stack Developer jobs in Irvine, CA are:

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Cities near Irvine, CA with the most Entry Level Full Stack Developer job openings:

Entry Level Full Stack Developer jobs near you
Infographic showing various Entry Level Full Stack Developer job openings in Irvine, CA as of August 2026, with employment types broken down into 91% Full Time, 4% Part Time, 4% Contract, and 1% Nights. Highlights an 83% Physical, 4% Hybrid, and 13% Remote job distribution, with an average salary of $130,750 per year, or $62.9 per hour.

Full Stack Developer / AI Focused

CarParts.com

Long Beach, CA • On-site

Contractor

Posted 3 days ago

New

CarParts.com rating

3.1

Company rating: 3.1 out of 10

Based on 5 frontline employees who took The Breakroom Quiz

Job description

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.

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