Top Entry Level Linux Jobs in San Diego, CA

General application for future opportunities at a remote open-source consulting company. Potential work includes software engineering, scientific computing, AI/ML, data engineering, DevOps, developer tooling, frontend development, technical writing, design, and community engagement. Quansight welcomes professional, research, educational, personal-project, and open-source experience, including early-career and internship interest.

Entry-level highway engineering role on the professional PE track. Prepare design computations, plans, specifications, reports; perform field inspections; ensure code compliance; maintain documentation; support QA/QC and project budgets.

Engineering Manager position with on-site work in Lancaster. The posting provides no specific responsibilities, qualifications, compensation, travel, sponsorship, or clearance details.

Supports quantum network deployments after handoff by maintaining fiber and optical hardware, commissioning customer sites, characterizing performance, troubleshooting physical and logical faults, and documenting configurations. Consults with researchers and customers to design loss-budgeted fiber paths that meet distance, polarization, and timing requirements. The role requires hands-on work with optical test equipment, photon detectors, datacenter hardware, and networking concepts, along with customer communication, flexible hours, and approximately 25% travel.

Build and ship software features and fixes for an AI-powered pharmacy operating system, including prior authorization workflows, Epic integrations, and AI agents supporting patient calls and texts. Responsibilities include independent development, testing, code reviews, debugging, learning pharmacy workflows, identifying UX issues, and using AI tools thoughtfully. This is a hybrid role requiring three days per week in the Orlando office.

Build and deploy full-stack software for military logistics customers. Own features from concept through deployment, integrate with existing systems, improve reliability and performance, collaborate across engineering and product teams, and work directly with end users. The role involves customer-facing engineering, operational problem solving, cloud and edge deployments, and approximately 25% travel for deployments, field testing, and support.

Supports the design and documentation of manufacturing facilities, equipment, infrastructure, and critical utility systems. Performs basic engineering calculations, process modeling, material and energy balances, equipment sizing, and fluid or heat transfer analyses under supervision. Develops process flow diagrams, P&IDs, equipment lists, datasheets, utility summaries, and related design documents. Collaborates with multidisciplinary teams, supports construction documentation and administration, and may conduct site observations and assist field installation teams.

Build realistic coding tasks involving feature implementation and bug fixes for AI systems. Develop deterministic verifiers that accept valid solutions and reject invalid ones. Apply backend expertise in scalable services and APIs, debugging, performance optimization, automated testing, code review, refactoring, and complex feature development. The role is remote, contractor-based, flexible, and output-compensated, with work expected to begin shortly after onboarding.

Deploys and maintains Python and R data science applications, dashboards, and API models in production. Responsibilities include designing CI/CD pipelines, managing containerized environments, implementing application and model monitoring, supporting cybersecurity and compliance requirements, troubleshooting runtime issues, and optimizing data science platform tooling. The role bridges data science development with enterprise IT infrastructure and requires an active Secret clearance.

Deploys and maintains Python and R data science applications, dashboards, and APIs in production. Responsibilities include designing CI/CD pipelines, managing containerized environments, monitoring application and model performance, supporting cybersecurity compliance, troubleshooting runtime issues, and optimizing data science platform tooling.