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Remote Software Engineer Jobs in Santa Ana, CA (NOW HIRING)

Clera

Software Engineer

Los Angeles, CA · Remote

$150K - $300K/yr

We're looking for a sharp, driven Software Engineer with 2-5 years of experience who thrives in fast-paced, zero-to-one environments and is eager to own core systems end-to-end. This is a high-impact ...

Ingram Micro

Sr. Software Engineer

Irvine, CA · On-site +1

$112K - $190K/yr

This position is based at our Irvine, California headquarters and follows a hybrid schedule, with three days per week in the office and two days remote. As a Sr. Software Engineer, you will lead the ...

Deep Sky

Software Engineer

Los Angeles, CA · On-site +1

We are looking for a Software Engineer to be the hands-on owner of our Direct Air Capture (DAC) platform's user experience. You will use React/TypeScript and strong product intuition to design and ...

Deep Sky

Software Engineer

Los Angeles, CA · Remote

We are looking for a Software Engineer to be the hands-on owner of our Direct Air Capture (DAC) platform's user experience. You will use React/TypeScript and strong product intuition to design and ...

Mantari

Software Engineer

Torrance, CA · On-site +1

Automate data ingest and feature engineering from our growing sensor network (satellite, buoy ... Experience with numerical weather prediction, remote-sensing data, or geospatial intelligence

BuildOps

Senior Software Engineer

Los Angeles, CA · On-site +1

$132K - $174K/yr

We're looking for a Senior Software Engineer to help build and scale the product experiences and systems behind that mission. In this role, you'll work across frontend applications, backend services ...

BuildOps

Senior Software Engineer

Los Angeles, CA · On-site +1

$132K - $174K/yr

We're looking for a Senior Software Engineer to help build and scale the product experiences and systems behind that mission. In this role, you'll work across frontend applications, backend services ...

Eliassen Group

Senior Software Engineer

Los Angeles, CA · Remote

$45 - $53/hr

Fully Remote until West LA office opens - then 3x a week onsite in Los Angeles, CA Our client seeks a Senior Software Engineer to build scalable, secure, and resilient platform services. You will ...

First American

Senior Software Engineer

Santa Ana, CA · On-site +1

$127K - $168K/yr

What We Do Remote Work Welcome Be part of a transformative team that is shaping the way First ... As a Senior Software Engineer, you will lead projects as part of a small, focused engineering ...

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Remote Software Engineer information

See Santa Ana, CA salary details

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How much do remote software engineer jobs pay per year?

As of Aug 23, 2026, the average yearly pay for remote software engineer in Santa Ana, CA is $153,510.00, according to ZipRecruiter salary data. Most workers in this role earn between $124,900.00 and $180,000.00 per year, depending on experience, location, and employer.

What is a remote software engineer?

Remote Software Engineers are professionals who design, develop, test, and maintain software applications from locations outside of a traditional office environment. They collaborate with teams and clients using digital communication tools, allowing for flexible work arrangements. Remote Software Engineers require strong technical and communication skills, as well as the ability to manage their own schedules and work independently. This role is ideal for individuals who are self-motivated and comfortable working in a virtual setting.

What does a remote software engineer do?

As a remote software engineer, you work from home to create and develop systems using programming languages and frameworks. As part of your duties, you design and install software solutions by determining specifications and developing code. You also improve software initiatives by reviewing systems and recommending solutions, often virtually guiding clients through the database, network, and computer processes. By collecting and analyzing issues, you can develop solutions for a variety of technical problems. The remote aspect of this job means you can work from anywhere with a reliable internet connection.

What are the key skills and qualifications needed to thrive as a remote software engineer, and why are they important?

To thrive as a Remote Software Engineer, you need strong programming skills, proficiency in software development methodologies, and typically a degree in computer science or related field. Familiarity with version control systems like Git, cloud platforms, and project management tools such as Jira is often required. Excellent communication, self-motivation, and time management are crucial soft skills for remote collaboration. These abilities ensure effective development, seamless teamwork, and productivity in a distributed work environment.

What are some common challenges faced by remote software engineers, and how can they be effectively managed?

Remote software engineers often encounter challenges such as communication barriers, time zone differences, and maintaining work-life balance. These can be effectively managed by utilizing collaboration tools (like Slack or Zoom), setting clear expectations with team members, and establishing a dedicated workspace. Regular check-ins, asynchronous updates, and proactive communication help ensure everyone stays aligned on project goals. Building strong relationships with colleagues through virtual meetings and team-building activities can also foster a supportive remote work environment.

What is the difference between Remote Software Engineer vs Remote Web Developer?

AspectRemote Software EngineerRemote Web Developer
Required CredentialsBachelor's in CS or related field, coding skillsBachelor's in CS, design, or related field, coding skills
Work EnvironmentCollaborates on software projects, often in teamsFocuses on website and web app development, often in teams
Employer & Industry UsageTech companies, startups, software firmsWeb agencies, tech companies, startups
Search & Comparison IntentOften compared for software development rolesRelated but more focused on web-specific tasks

Remote Software Engineers develop a wide range of software applications, while Remote Web Developers specialize in building websites and web-based applications. Both roles require similar technical skills and often work in similar environments, but their focus areas differ, making this comparison useful for those exploring career options or job opportunities in tech.

More about Remote Software Engineer jobs

What are the most commonly searched types of Software Engineer jobs in Santa Ana, CA?

The most popular types of Software Engineer jobs in Santa Ana, CA are:

What are popular job titles related to Remote Software Engineer jobs in Santa Ana, CA?

For Remote Software Engineer jobs in Santa Ana, CA, the most frequently searched job titles are:

What job categories do people searching Remote Software Engineer jobs in Santa Ana, CA look for?

The top searched job categories for Remote Software Engineer jobs in Santa Ana, CA are:

What cities near Santa Ana, CA are hiring for Remote Software Engineer jobs?

Cities near Santa Ana, CA with the most Remote Software Engineer job openings:

Remote Software Engineer jobs near you
Infographic showing various Remote Software Engineer job openings in Santa Ana, CA as of August 2026, with employment types broken down into 1% As Needed, 85% Full Time, 10% Part Time, and 4% Contract. Highlights an 88% Physical, 4% Hybrid, and 8% Remote job distribution, with an average salary of $153,510 per year, or $73.8 per hour.

Software Engineer - AI Systems (Go)

Stanbridge University

Irvine, CA • On-site, Remote

Full-time

Posted 2 days ago

New

Job description

Stanbridge University is seeking Software Engineers - AI Systems (Go) to design, build, and operate production AI systems that perform complex, multi-stage work reliably and at scale.
This is a hands-on engineering role focused on a challenging class of problems: long-running AI workflows that call models, tools, and external APIs; maintain state across extended executions; produce structured content and generated media; interact with human reviewers; recover from partial failures; and consistently deliver accurate results to users.
The core engineering challenges extend well beyond prompt development. These systems must account for provider failures and rate limits, interrupted workflows, changing state, non-deterministic model behavior, incorrect or unsupported outputs, variable latency and cost, and deployments occurring while work is in progress.
The successful candidate will combine strong software and distributed-systems engineering judgment with practical experience building and operating LLM-backed applications, AI agents, or agent-based systems in production.
You will join at a stage where significant architecture remains to be designed and built. Engineers in this role will have substantial ownership over the patterns, services, infrastructure, and engineering standards that shape the University's AI systems.
Remote Work Eligibility
This position is eligible for remote work for candidates residing in states where Stanbridge University is currently authorized to employ remote employees. Eligible states currently include: Arizona, Colorado, Illinois, Indiana, Kansas, Kentucky, Louisiana, Maryland, Michigan, Minnesota, Nevada, New Jersey, North Carolina, North Dakota, Ohio, Tennessee, Texas, and Wisconsin.
Candidates must reside in an eligible state at the time of employment. Remote-work eligibility is subject to University employment requirements and may change based on applicable state requirements.
Engineering Environment
The engineering problems addressed by this team include:
  • Durable workflow orchestration: Long-running, multi-stage pipelines using persistent state, job queues, checkpoints, resumability, idempotent execution, and recovery from interrupted or orphaned work.
  • Multi-provider model infrastructure: Routing across commercial model providers with model registries, token and cost controls, rate-limit handling, retries, circuit breakers, health monitoring, and provider failover.
  • Agent and tool orchestration: Systems in which AI agents interact with tools, APIs, data sources, application services, and deterministic business logic to complete multi-step work.
  • Prompt engineering infrastructure: Treating prompts as version-controlled production artifacts with review, testing, regression protection, and measurable behavior.
  • Testing non-deterministic systems: Recorded and replayable provider interactions, deterministic fixtures, evaluation harnesses, baselines, and regression testing for AI behavior.
  • Correctness and quality controls: Structured-output validation, automated evaluation, domain-specific requirements, evidence checking, and safeguards against confident but incorrect model output.
  • Human-in-the-loop workflows: Review and approval stages within automated processes, including systems capable of safely responding when users modify state or inputs during execution.
  • Generated media pipelines: Systems capable of producing and managing documents, audio, imagery, video, and other generated assets.
  • Production service architecture: Go-based APIs and backend services, relational data stores, job infrastructure, observability, and integrations supporting user-facing applications.
  • Secure ingestion: Processing user-supplied and potentially untrusted documents while maintaining appropriate security and authorization boundaries.
Two Engineering Emphases
Engineers will meet the same overall technical bar but may bring deeper expertise in one of two areas:
Platform and Pipeline
Focused on distributed systems, workflow orchestration, durable state, job queues, recovery, provider infrastructure, latency, throughput, scalability, reliability, and cost optimization.
This emphasis is particularly well suited for experienced systems engineers who view AI models as powerful components that introduce a new set of distributed-systems and reliability challenges.
Agents and Quality
Focused on agent and tool architecture, prompt systems, context management, evaluation frameworks, regression testing, judge models, evidence validation, and end-to-end output quality.
This emphasis is particularly well suited for engineers who approach AI behavior through experimentation, measurement, testing, and systematic improvement.
Candidates may indicate an area of preference; however, specialization in one area is not required.
Essential Functions
  • Design, develop, test, deploy, and operate production-quality software and backend services, primarily using Go (Golang).
  • Architect and build AI agents and agent-based systems capable of using tools, interacting with APIs, maintaining state and context, and executing complex multi-step workflows.
  • Design durable workflows that can checkpoint, resume, retry, recover, and safely continue execution following partial failures or system interruptions.
  • Determine how models, agents, tools, APIs, data sources, services, and deterministic application logic should divide responsibilities within an AI system, including recognizing when an AI model is not the appropriate solution.
  • Design integrations across multiple AI model providers, including routing, failover, rate-limit management, health monitoring, and degradation strategies.
  • Build safeguards including structured-output validation, error handling, retries, quality gates, evidence validation, and automated recovery mechanisms.
  • Develop testing strategies for non-deterministic AI behavior using techniques such as deterministic fixtures, recorded and replayed interactions, regression suites, evaluation harnesses, and quality baselines.
  • Develop observability capabilities including tracing, metrics, logging, evaluation data, and replayable execution histories to support production debugging and performance analysis.
  • Design human-in-the-loop workflows incorporating review, approval, intervention, and modification of workflow state.
  • Build and maintain APIs, backend services, relational data models, job-processing infrastructure, and supporting application components.
  • Develop secure methods for ingesting and processing user-supplied documents and other external content.
  • Optimize systems for reliability, latency, throughput, scalability, output quality, and cost per execution.
  • Build reusable engineering patterns and shared components that simplify the addition of new agents, model providers, tools, workflows, and output types.
  • Own technical problems from initial investigation and architecture through implementation, deployment, monitoring, troubleshooting, and ongoing production operation.
  • Collaborate directly with product stakeholders and domain experts to translate qualitative requirements into measurable system behavior and technical solutions.
  • Contribute to architectural decisions and engineering standards for AI-powered applications across the University.
Qualifications
Required
  • Substantial professional experience developing and operating production software, with strong experience in Go (Golang) or demonstrated depth in another backend language with the ability to become productive in Go quickly.
  • Hands-on experience shipping an LLM-backed application, AI agent, or agent-based system into production and supporting it after deployment.
  • Strong understanding of AI application architecture, including how models, agents, tools, APIs, data sources, services, and application logic interact.
  • Demonstrated distributed-systems engineering knowledge, including concurrency, asynchronous processing, queues, idempotency, retries, partial failure, state management, and recovery.
  • Experience designing systems that remain reliable when individual services, providers, or workflow stages fail.
  • Demonstrated testing discipline for systems involving non-deterministic behavior.
  • Strong experience designing and consuming HTTP APIs.
  • Experience with relational databases, SQL, and persistent application state.
  • Experience building, deploying, monitoring, and troubleshooting backend services in production environments.
  • Understanding of software architecture, testing, debugging, observability, and production engineering practices.
  • Ability to independently own ambiguous technical problems from investigation through production implementation.
  • Strong analytical judgment and the ability to balance reliability, quality, performance, complexity, and cost.

Preferred Qualifications
  • Experience with AI agent frameworks, orchestration patterns, or custom agent architectures, including an understanding of when a framework may not be appropriate.
  • Experience with retrieval-augmented generation (RAG), embeddings, vector databases, semantic search, or knowledge-retrieval architectures.
  • Experience developing evaluation and observability systems for LLM applications, including tracing, regression suites, quality dashboards, or automated evaluation.
  • Experience with judge models, structured-output validation, evidence checking, or other AI quality-control mechanisms.
  • Experience designing human-in-the-loop workflows involving review, approval, intervention, or modification of active workflow state.
  • Experience with document processing, headless-browser rendering, text-to-speech, image generation, video generation, or other media pipelines.
  • Experience with event-driven architectures, durable job queues, and asynchronous processing at scale.
  • Experience with containers, cloud infrastructure, CI/CD, and production deployment environments.
  • Understanding of prompt injection, authorization boundaries, data isolation, and security considerations when untrusted content is processed by AI systems.
  • Experience developing systems in environments where the accuracy of generated output carries significant operational, regulatory, compliance, or safety implications.
What Success Looks Like
An exceptional engineer in this role will:
  • Build AI workflows that operate reliably in production without requiring routine human intervention.
  • Design systems that recover gracefully from model-provider failures, interrupted execution, deployments, rate limits, and other partial failures.
  • Make AI behavior increasingly measurable, reproducible, testable, and observable rather than relying on subjective evaluation.
  • Improve output quality while systematically reducing latency and cost per execution.
  • Create durable engineering patterns that make subsequent agents, providers, workflows, and output types easier and safer to introduce.
  • Identify when deterministic software should replace or constrain model-driven behavior.
  • Build systems whose failures can be diagnosed through instrumentation and replay rather than guesswork.
  • Establish architecture and engineering practices that become foundational components of the University's broader AI capabilities.
Compensation
Compensation is based on education, experience, and qualifications and internal equity
Conditions of Employment:
  • A job-related assessment may be required during the interview process.
  • Must be able to perform each essential duty satisfactorily and be physically present in the office (unless otherwise noted).
  • Employment Authorization: Applicants must be legally authorized to work in the United States. Stanbridge University does not provide employment-based immigration sponsorship or participate in employer-sponsored or employer-dependent work authorization programs.
  • Sponsorship: Sponsorship now or in the future could include having Stanbridge University sponsor, complete employer documentation, provide attestations or training plans, or otherwise participate in a work-authorization or employment-based immigration program in order for an individual to begin or continue employment. Stanbridge University does not provide any such sponsorship.
  • Employment verification will be conducted to validate work experience per accreditation standards.
  • Offers of employment are contingent upon the successful completion of a background check.
  • Official transcripts are required prior to hire. Degrees earned outside the United States must be evaluated by a recognized credential evaluation service to determine U.S. degree equivalency and applicable subject-area coursework.
  • Reasonable accommodations may be made to enable individuals with disabilities to perform essential functions.
Work Environment
  • Standard professional and technology-focused work environment.
  • Duties are typically performed while sitting at a desk or computer workstation.
  • Position requires extensive interaction with computers, software development environments, cloud services, AI systems, and technical infrastructure.
  • Subject to collaboration with cross-functional teams, changing technical requirements, and demanding project timelines.
Physical Demands
  • Regularly sits for extended periods.
  • Physical ability to perform department-related dutie

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