Top Remote Python Jobs in Washington, DC

Lead embedded Linux platform enablement for Nabu Casa hardware: maintain and improve Buildroot-based OS support, perform board bring-up, debug platform-level issues, optimise performance and reliability, upstream patches to open-source projects, maintain CI for builds, and document platform architecture and procedures while collaborating with hardware teams and open-source communities.

Define and drive full-stack architecture and technical vision across teams, write high-quality production code, lead zero-to-one projects, productionize ML systems, own reliability and SLOs, mentor senior engineers, and align engineering roadmaps with product and company strategy.

Lead and evolve secure, automated cloud platforms and CI/CD pipelines using IaC, containers, and Kubernetes. Integrate security tooling, mentor engineers, optimize cloud cost and reliability, and produce technical documentation while supporting Generative AI development workflows.

Lead backend and full-stack engineering strategy, design and deliver scalable, secure distributed systems and microservices for government customers. Drive architecture, data design, observability, CI/CD, cloud-native patterns, mentoring, and large modernization initiatives while collaborating with product and stakeholders.

Design, develop, and document Salesforce solutions (Apex, LWC, Visualforce) and integrations; lead small developer teams; build APIs, data migrations, and test plans; ensure code quality and mentor others while evaluating and adopting AI-enabled tools.

The Senior DevOps Engineer will build secure, scalable infrastructure on AWS, manage Kubernetes platforms, and oversee FedRAMP-related tasks while ensuring compliance and operational efficiency.

As a Senior SDET, you will enhance testing practices, improve CI pipelines, mentor engineers, and ensure platform reliability through comprehensive testing strategies.

Manage a team of cloud engineers to govern and optimize a multi-cloud environment while implementing security frameworks and managing vendor relationships.

The Site Reliability Engineer will lead technical initiatives, architect solutions, troubleshoot issues, mentor team members, and improve observability practices.

Develop, enhance, and maintain enterprise web-based GIS applications, dashboards, APIs, and geospatial services. Integrate GIS with enterprise systems, support ArcGIS Enterprise, assist modernization to cloud, perform testing, troubleshooting, deployment, documentation, and user support within federal environments.

Lead a small team owning GFiber's multi‑terabit backbone design, reliability, capacity planning, and observability across IP and optical layers. Drive DWDM metro/long‑haul transport design, telemetry pipelines, automation tooling (Go/Python), and align team strategy with organizational goals while remaining hands‑on in design and code.

The Cloud Administrator manages cloud infrastructure and services, ensuring their performance, security, and reliability while troubleshooting issues and optimizing costs.

Lead technical design and architecture of large-scale Ceph clusters, drive capacity planning and major upgrades, resolve complex cross-functional production incidents, establish automation and reliability standards, and mentor engineers on advanced Ceph operations to scale the global storage platform.

Own and maintain reliability, performance, scalability, and capacity of massive Ceph storage clusters. Troubleshoot distributed storage issues, build automation (Python/Shell, SaltStack, Ansible), define observability (SLIs/SLOs, PromQL/LogQL, Grafana), and lead lifecycle initiatives like upgrades, expansions, and migrations.

Provide system administration and operational support for lottery platforms: implement new customers, troubleshoot availability/performance, monitor capacity and security, manage incidents and SLAs, maintain monitoring/KB articles, and participate in a 24x7 on-call rotation. Work on product releases, enhancements, and upgrades across Linux, databases, containers, and cloud environments.

Build and evolve Nexus interfaces for Temporal SDKs across multiple languages. Design and implement runtime APIs and behaviors, integrate SDKs tightly with language runtimes for durable, deterministic execution, collaborate with OSS community, write technical documentation, and own end-to-end features with high reliability.

The Staff Machine Learning Engineer will develop and maintain MLOps tools, optimize model performance, and collaborate on ML system architecture with a focus on enhancing developer experience.

Senior technical authority for IBM AIX and Power Systems architecture, engineering, migration, performance tuning, automation, and resiliency. Lead large-scale OS and hardware migrations, own lifecycle management, incident escalation/RCA, HA/DR design, and mentor L3 engineers while ensuring governance, compliance, and vendor coordination.

Develop and maintain backend solutions, ensuring performance, security, and regulatory compliance, while collaborating in cross-functional teams.

Lead and deliver complex customer implementations, debug and build integrations, drive architectural decisions, run technical discovery, mentor junior engineers, and write production/integration code (Python, JavaScript, SQL). Serve as technical escalation and represent FDE in product and cross-functional discussions.

Lead enterprise data architecture and platform engineering efforts using Databricks Lakehouse. Design scalable cloud-native data platforms, implement medallion architectures, enable data governance and AI/ML workloads, build automation and CI/CD for Databricks, and mentor teams while collaborating with stakeholders on strategy and delivery.

Partner with customers to deploy collaborative robots: perform onsite hardware/software testing, troubleshoot and repair, conduct readiness assessments, document procedures, replace parts/retrofits, and drive project communication to ensure successful long-term adoption.

Build and maintain full-stack product features, backend services, APIs, data pipelines, and AI integrations to transform customer interaction data into AI-powered experiences. Collaborate with cross-functional teams to design, test, and support production systems, internal tools, and user-facing interfaces. Own ambiguous problems end-to-end and raise engineering quality across the data and AI platform.

Design, build, and operate reliable backend systems while mentoring engineers, participating in design discussions, and improving system performance.

Embedded with strategic customers, own end-to-end design, build, and production rollout of cloud infrastructure for large-scale GPU/HPC AI workloads. Build compute orchestration, platform services, onboarding sandboxes, and reliability/security tooling. Turn field learnings into reusable platform capabilities and partner with Product and Engineering to productize them.