Top Remote Python Jobs in Washington, DC

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.

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.

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.

The Lead Systems HPC Engineer will optimize GPU clusters' performance, troubleshoot issues, integrate new hardware, and collaborate with teams on system-level software development.

Design and build systems for managing bare-metal infrastructure, focusing on automation, hardware provisioning, and software integration with Linux systems.

Designs and implements middleware, APIs, and data exchange to integrate Appian, Palantir, and VA financial systems. Builds secure, auditable integrations, supports ATO/FedRAMP activities, develops tests and runbooks, troubleshoots production issues, and mentors junior engineers.

As a Machine Learning Engineer in Computer Vision, you'll develop AI solutions using advanced algorithms, focusing on image classification, segmentation, and video processing tools.

The ML Engineer will leverage deep learning for natural language processing tasks, impacting Entefy's products while requiring strong machine learning fundamentals and NLP expertise.

Embed with strategic clients to design, build, and ship production Python code and data pipelines. Integrate RESTful APIs, optimize complex SQL, architect scalable data workflows, lead technical discovery and workshops, and translate operational needs into product improvements while ensuring mission-critical outcomes.

Design, build, and scale low-latency distributed platform components; define technical vision and architecture; collaborate with product and ML teams; evaluate build-vs-buy decisions; prototype research breakthroughs and deliver robust, production-grade services.

Build and own production backend services in Python for a large-scale ecommerce search platform. Work with SQL-heavy systems, monitoring/alerting, and debugging user-facing systems. Deliver maintainable software via tests, code review, and incremental delivery. Collaborate on data integrations and optional ETL/ELT workflows with PySpark; apply SEO, API/auth, and unstructured data knowledge when needed.

Build and maintain Python backend services for experiment assignment, logging, and report generation. Improve scalability, performance, and reliability of experiment reporting pipelines. Add product-facing UI features, take technical ownership of projects, and participate in weekly on-call rotation. Collaborate with Search/Recommendations, ML/DS, and customer-facing teams.

Lead design and development of multi-tenant backend services and APIs for a space communications platform on AWS. Build scalable, secure services with automation, testing, and mentorship of junior engineers; interact with customers and partners and support operations for US government satellites.

Build and lead development of core backend platform services for a space communications ground system. Design and implement multi-tenant cloud-native APIs and services with automation and security, own features end-to-end, mentor junior engineers, and collaborate across teams to support US government satellite operations.

Responsible for managing wafer level testing processes to improve yield, overseeing test equipment, and coordinating with external suppliers and engineering teams.

Lead the qualification and process development for a new InP photonics semiconductor fabrication facility, focusing on process integration and yield improvement.

The Senior Software Engineer will enhance Attentive's event streaming platform, architect high-throughput systems, improve tools for product teams, and tackle complex distributed challenges. Responsibilities include collaborating on messaging systems and modernizing the architecture.

Lead verification testing for high-speed coherent and direct-detect photonic components and subsystems. Design test plans/algorithms, execute module performance validation, and collaborate with PIC, ASIC, module, and system teams to ensure quality product releases.

Own and improve the end-to-end developer experience for AIMS ML teams: local/remote dev environments, build/test infrastructure, CI/CD, and internal platforms. Design, build, and operate large-scale build and CI systems, remove developer friction, instrument workflows, drive adoption, and set technical standards. Partner with ML researchers to prioritize productivity investments and evaluate/integrate GenAI-powered developer tooling with safety guardrails.

Design, build, and maintain FinOps tooling, data pipelines, dashboards, and automation to improve cloud cost visibility, allocation, and optimization across Azure, AWS, and GCP. Support budgeting, variance analysis, anomaly detection, and executive reporting. Collaborate cross-functionally to implement tagging, chargeback/showback, governance, and AI-enabled automation while documenting operational processes for scalable FinOps practices.

Own and scale the search, indexing, and data-scanning infrastructure for petabyte-scale data. Optimize hybrid lexical and vector retrieval for sub-second latency, design cost-effective hot/warm/cold data tiering and massive asynchronous scans, drive down latency and cost, and provide technical leadership for resilient, highly available indexing and retrieval systems.

Build and maintain developer platform tooling that ensures SIL-4 safety compliance: automation for certification evidence, CI/CD improvements, Bazel build maintenance, lightweight web dashboards, developer workflow automation, monitoring, and on-call support.

Own and improve uptime for backend services, APIs, workers, and ML pipelines; build monitoring, alerting, auto-remediation, optimize GCP infrastructure and Postgres performance, run on-call rotation and blameless postmortems.