Top Remote Python Jobs in San Diego, CA

The Staff Quality Engineer will establish testing strategy, expand scalable end-to-end automation, close coverage gaps, and define quality metrics and release gates. They will test AI-powered features, collaborate with developers on test planning, use AI tools for testing and triage, and coach engineering teams on quality-by-design practices. The role requires influencing multiple teams independently without a dedicated QE team or manager.

Work on real-world web applications across frontend and backend. Use modern frontend frameworks (React/Angular/Vue) and backend tech (Node.js, Python, Java, PHP). Integrate RESTful APIs with databases (MongoDB, PostgreSQL, MySQL), debug, test, optimize performance, and collaborate with designers and product teams while gaining hands-on mentorship.

Design and build robust backend services and secure APIs for finance and reader applications. Implement authentication, payments, subscriptions, and finance workflows. Design Postgres/Redis schemas, integrate with financial APIs (BSE Star MF, NSE) and DRM services, and ensure compliance-friendly data handling.

Own and evolve production infrastructure for AI workloads, including Kubernetes, cloud-native migrations, scheduling, orchestration, observability, infrastructure as code, deployment workflows, security, reliability, and incident recovery. Partner with engineering and business operations teams to build dependable platform capabilities. The role requires deep production Kubernetes experience, strong cloud and Linux fundamentals, architectural judgment, automation skills, and end-to-end ownership of infrastructure systems.

Build Tailscale’s internal AI-enablement tools, workflows, guardrails, and documentation. Partner with engineering leadership and teams to define the AI roadmap, evaluate coding agents and models, improve adoption, and measure effects on velocity and quality. The role involves developing internal developer-facing automation, teaching and influencing engineers, identifying high-leverage AI opportunities, and promoting safe use of AI-generated code in a distributed engineering organization.

Designs scalable, secure, resilient cloud and data architectures for high-volume services. Evaluates cloud technologies, develops reusable Terraform infrastructure and automation, and serves as a database architecture expert for PostgreSQL, Oracle, MongoDB, Redis, and Valkey. Builds prototypes and reference implementations, designs highly available distributed database systems, coaches engineers, partners with product and SRE teams, and converts validated concepts into production-ready architecture patterns.

Design, build, and operate backend services supporting external penetration testing, including Python APIs, workflow orchestration, ETL pipelines, AWS data lakes, GraphQL and graph APIs, Kubernetes infrastructure, and observability systems. Own service performance, scalability, uptime, deployment, and production operations while collaborating with product, design, and frontend teams. Contribute to architecture, technical documentation, platform stability, and user-facing launches.

Design, operate, and evolve production GCP infrastructure, including GKE clusters, networking, IAM, shared services, and Terraform automation. Troubleshoot complex Kubernetes, Linux, networking, DNS, and distributed-systems issues; improve observability, security, scalability, reliability, and cost efficiency. Lead upgrades, migrations, architecture improvements, technical reviews, and mentorship while partnering with Platform Engineering, SRE, and Security teams.

Develop and optimize process models and techno-economic analyses for direct air capture demo and production facilities. Translate Aspen models into equipment sizing, cost estimates, process documentation, and vendor-ready specifications. Refine heat and mass balances, evaluate operating scenarios, support equipment selection, and identify energy, capital, and operating cost reductions. Collaborate with engineering, R&D, project management, vendors, and external design firms while presenting technical and financial findings to leadership, investors, and stakeholders.

Build and manage secure cloud platforms, automated deployments, infrastructure as code, Kubernetes environments, and self-service developer capabilities. Administer Linux systems, troubleshoot production services, manage accounts and documentation, and collaborate with developers to resolve issues. The role includes operational ownership, on-call support, customer collaboration, AI-augmented delivery, and up to 25% travel. U.S. citizenship and the ability to obtain a security clearance are required.

Provide technical leadership for site reliability engineering, applying software engineering practices to large-scale operations. Design automated, repeatable operational solutions; monitor service objectives, application performance, and business metrics; improve resiliency, efficiency, and cost. Support cloud and on-premise infrastructure, production applications, deployment automation, testing, and vulnerability response. Partner with software development, security, and business teams while promoting Agile practices, change management, training, and operational health.

Designs, builds, and productionizes healthcare knowledge graph solutions using LPG and RDF technologies. Develops FHIR data transformation and ingestion pipelines, ontology mappings, entity linking, provenance tracking, graph queries, and automated validation. Translates semantic specifications into scalable implementations while collaborating with ontology, terminology, clinical, FHIR, and data engineering teams. Participates in technical design, architecture reviews, code reviews, and engineering best practices.

Lead client cloud architecture engagements, design and implement scalable AWS infrastructure, and guide re-platforming and modernization initiatives. Build Infrastructure as Code, observability, monitoring, automation, and security solutions using AWS and partner tools. Lead cross-functional teams, establish runbooks, conduct code reviews, coach engineers, engage clients, and support continuous improvement. The role also involves technical presentations, documentation, vendor relationships, open-source contributions, and occasional travel.

Designs and maintains cloud infrastructure for game development and live-service games. Responsibilities include infrastructure-as-code, cloud security, CI/CD pipeline development, live-service support, development environment automation, monitoring, profiling, and troubleshooting. The role requires Azure expertise, scripting, containerization, orchestration, code compilation, and familiarity with game packaging and deployment workflows.

Own backend services from design through production support, solve ambiguous technical problems, improve reliability, build automation, and contribute to architecture and technical decisions. The role involves developing secure software in a regulated healthcare environment, debugging production issues, writing tests, mentoring engineers, and using modern cloud, deployment, observability, and AI-assisted development tools.

Design and validate complex engineering simulation problems for training and evaluating AI models and coding agents. Develop Python-based simulation workflows, automated tests, reference solutions, and grading systems across electrical, mechanical, and aerospace domains. Analyze AI execution logs and failure modes, validate physical plausibility and numerical convergence, refine benchmarks, and collaborate with engineering and AI research teams. This remote contractor role requires approximately 40 hours weekly, Pacific Time overlap, and weekend on-call availability.

Build and scale backend systems, APIs, infrastructure, and applications for multimodal sensing and AI intelligence platforms. Responsibilities include processing sensor, audio, video, and environmental data; supporting data pipelines and model inference; ensuring reliability, scalability, and data integrity; and contributing to engineering standards and long-term technical strategy.

Develop and debug production-grade full-stack applications across front-end and back-end systems. Build features, APIs, databases, and data models; troubleshoot end-to-end issues; analyze unfamiliar codebases; translate implementations across programming languages; and review AI-generated and developer-written code for correctness, security, scalability, performance, and maintainability.

Develop and evaluate coding tasks for AI systems, including bug fixes, feature development, refactoring, optimization, reproducible environments, reference solutions, automated testing, and deterministic evaluation. The role also involves debugging complex codebases, reviewing contributions, documenting technical decisions, and refining evaluation criteria. Candidates should have strong software engineering fundamentals, backend or API development experience, open-source contributions, and proficiency in at least one listed programming language.

Build and maintain software engineering infrastructure for AI development and evaluation workflows. Develop rubrics, review and label preference data, create scalable pipelines, document findings, navigate complex codebases, and improve evaluation quality and consistency. Collaborate with researchers and engineers in an ambiguous, fast-paced environment while applying strong judgment, debugging, communication, and system-design skills.

Evaluates AI-generated software for correctness, quality, efficiency, security, and maintainability. Designs programming challenges, reference solutions, test cases, and evaluation criteria. Reviews technical reasoning related to debugging, architecture, and system design, then provides structured feedback and documentation. The role requires strong programming fundamentals, software testing and code review experience, analytical problem-solving ability, and clear technical communication.

Provides technical leadership for Azure SQL databases supporting a highly available Azure service. Responsibilities include database performance tuning, reliability and capacity planning, schema changes, data migrations, observability, incident response, root cause analysis, operational automation, and technical strategy. The role partners with software engineers, SREs, and architects to improve scalability, durability, cost efficiency, and long-term service reliability across teams.

Principal Site Reliability Engineer responsible for the availability, scalability, durability, and operational reliability of production systems. The role leads observability, incident response, SLO and error-budget practices, automation, infrastructure as code, and long-term service improvements. Responsibilities include designing monitoring and alerting frameworks, supporting Kubernetes and distributed storage environments, improving operational processes, collaborating across engineering and business teams, and mentoring reliability-focused practices. The position requires extensive experience with Linux, cloud infrastructure, containers, scripting, storage systems, and large-scale production environments.

Design, deploy, maintain, tune, and secure enterprise Linux environments supporting high-performance EDA compute farms. Lead infrastructure-as-code automation, configuration management, continuous deployment, and tooling development using Python, Bash, or Go. Manage LDAP/SSSD naming services, NFS and Autofs storage, virtualization, container infrastructure, registries, networking, and infrastructure telemetry. Support highly available, scalable systems and potentially integrate cloud platforms, CI/CD pipelines, EDA toolchains, and HPC clusters.

Lead architecture and roadmap for Azure PostgreSQL systems; design and review scalable, maintainable database engine features (query processing, storage, transaction logging, recovery, versioning, IO, scale-out). Influence coding standards and run code reviews, write proof-of-concept or production code, contribute to open-source PostgreSQL/Linux, and participate in rotational on-call operations.