Top Remote Python Jobs in San Diego, CA

Build and deliver AI-assisted QA curriculum as part-time instructors, authors, or subject-matter experts. Create and review course content, lead workshops, design exercises, advise on AI QA workflows, and support curriculum improvement with hands-on tool experience.

Lead and scale the product-facing engineering organization for consumer fintech products. Hire and grow teams, drive architecture and code quality, embed AI-native tooling and practices, and ship member-facing voice/chat/BankGPT experiences while remaining hands-on in technical reviews and architecture.

Design, build, and operate CI/CD, Kubernetes-based platforms, deployment automation, and observability for engineering workflows. Improve reliability, performance, and scalability across cloud and on-prem environments, debug cross-boundary failures, perform root-cause analysis, and deliver durable platform software and self-service tooling.

Develop, optimize, and validate high-performance ML and linear-algebra kernels for Cerebras wafer-scale hardware. Implement low-level assembly and a C-like DSL (CSL), model and analyze performance, build testing methodologies, and collaborate with chip and system architects to maximize compute utilization for state-of-the-art AI and HPC workloads.

Operate and scale production AI inference infrastructure, run releases and capacity changes, build self-service CD pipelines and automation, extend telemetry and observability, collaborate on SLOs, post-mortems, and capacity planning to reduce operational toil.

Design, implement, and maintain Python frameworks and services that orchestrate distributed engineering workflows across machines and clusters. Build scheduling, execution, resource management, failure recovery, and test infrastructure. Define APIs and abstractions, reason about concurrency and distributed-systems behavior, debug complex multi-system issues, write automated tests and documentation, and partner with platform, CI, release, QA, and product teams to deliver scalable infrastructure.

Design and implement system-level debugging, validation, and observability platforms. Build automated anomaly collection/analysis, visualization and root-cause tools, failure classification and monitoring frameworks. Extend compilers, runtimes and programming interfaces for profiling and instrumentation, improve bring-up and low-level debug workflows, lead cross-functional initiatives, support incident response, and establish debuggability and reliability best practices.

Early-career SDET on Release Integration Testing for Cerebras AI Inference Core. Write automation and tests across the AI stack, triage cross-stack failures, maintain branch stability, collect qualification evidence, and collaborate with feature and infra teams to prepare features for production release.

Design and develop software to automate bare-metal configuration, orchestration, scheduling, and job placement for large wafer-scale clusters. Build upgrade/patch workflows, monitoring, HA failure handling, visualization and alerting, and user/admin tools. Ensure on-premise and cloud deployment support and develop tests and debugging tools for distributed systems.

Design, build, test, and deploy scalable front-end and back-end software for a remote literacy assessments team. Collaborate with product, design, data science, and DevOps to improve APIs, reporting, and classroom reliability. Participate in code review, automated testing, deployment automation, monitoring, and cross-functional learning to deliver equitable education tools.

Design, build, and operate production-grade OpenStack platforms including compute (Nova), networking (Neutron), and Ceph storage. Implement HA, capacity planning, virtualization tuning, SDN integrations, automation (Ansible/Terraform), Kubernetes-based control planes, bare-metal provisioning (Ironic), observability, upgrades, and on-call/night-shift support.

Support and improve production SaaS infrastructure across AWS, colocation, and hosted platforms. Administer Windows and Linux systems, virtualization, storage, networking, and database support. Lead incident troubleshooting, root cause analysis, monitoring improvements, vulnerability remediation, automation initiatives, and medium-sized infrastructure projects. Collaborate on compliance (SOX/PCI/HIPAA), disaster recovery, and documentation to increase operational reliability.

Build bespoke, mission-critical full-stack software for strategic (defense) customers: front-end, back-end, data integration, cloud (AWS/GCP), and CI/CD. Translate customer needs into technical requirements, mentor engineers, leverage AI tooling for rapid iteration, and deliver reliable solutions while traveling to customer sites.

The Software Engineer in Platform Operations designs and manages core infrastructure, ensuring secure, scalable, and reliable platform operations while collaborating with software engineers to improve deployment experiences.

As a Full Stack Software Engineer at Doxel, you will design and develop full-stack applications, collaborating cross-functionally to optimize user experiences and facilitate data processing for construction projects.

The Lead Software Engineer will design and develop full-stack applications for construction project management, ensuring intuitive data workflows and collaborating across teams to enhance user experience and performance.

Customer-facing senior Solutions Architect leading and mentoring engineers to design and deliver cloud-native, secure CI/CD solutions. Own POCs and packaging (Zarf/UDS), build CI/CD pipelines, deploy and troubleshoot containers and Helm/Kustomize artifacts across AWS/GCP/Azure, and drive process improvements and enablement for Defense customers.

Design and develop complex technical support solutions, reproduce and track customer issues, recommend product enhancements, produce technical documentation, collaborate on POCs, and manage networking, cloud, virtualization, backup/recovery and Kubernetes across AWS, Azure, and GCP. Use container platforms (OpenShift, PKS, Rancher, GKE, EKS), VMware, automation scripting (Bash/Python), and CRM/issue tools (Salesforce, ServiceNow, JIRA) to resolve escalations and improve product adoption and lifecycle upgrades.

Design, build, and maintain scalable infrastructure and CI/CD pipelines for TensorRT Edge-LLM across embedded and desktop GPU/CPU platforms. Configure and operate tools (CMake, GitLab, GitHub Actions, Kubernetes, Docker), monitor systems, automate builds/tests, address security CVEs, and collaborate with autonomy and external partners to improve development velocity and reliability.

Lead end-to-end design, build, and operation of large-scale data pipelines on GCP/BigQuery, optimize data models and queries, integrate legacy SQL Server/SSRS systems, implement ETL/ELT and data quality frameworks, use intermediate Python for automation, and support BI/reporting with Power BI/Looker/Tableau while promoting AI tool adoption.

Design and perform physical layout for silicon interposers using Cadence Virtuoso; run DRC/LVS and verification (Calibre, ICV, Thunder); perform feasibility studies, floorplanning, custom layout, and collaborate with package, SI, and technology teams to meet schedule, cost, manufacturability, and electrical goals.

As an AWS Software Engineer II, you will develop microservices, support legacy systems, and guide junior engineers, while improving operational workflows in an Agile team.

Lead full-stack engineering for a B2B partner portal: design and deploy React/TypeScript front ends, .NET services, and Python data pipelines (Databricks). Integrate LLMs and agentic development into product and workflows, mentor engineers, ensure quality engineering practices, and resolve production issues end-to-end.

Provide mid/senior-level application DBA support for MySQL and PostgreSQL: participate in Agile teams, design data models, maintain DB environments, implement HA/DR and replication, optimize performance, lead production incident response, manage backups/patching/changes, and mentor junior DBAs.

Lead reliability, observability, and resilience for cloud-based financial SaaS. Define SLOs/SLIs, design monitoring/tracing, own incident response and runbooks, build IaC and automation, implement AIOps, perform chaos and load testing, and write production-grade Python tooling while ensuring security and compliance.