Top Senior Linux Jobs in Washington, DC

Own the architecture, implementation, and operational excellence of major Azure Boost DPU software components. Develop low-level systems software spanning operating systems, virtualization, networking, storage, device management, and hardware interfaces. Lead complex cross-functional initiatives, optimize hyperscale performance and reliability, resolve system-level issues, establish engineering practices, influence platform architecture, and mentor engineers across Azure infrastructure teams.

Design, operate, and improve Recharge’s cloud-native infrastructure platform. Responsibilities include Kubernetes and GCP infrastructure management, troubleshooting production issues, maintaining observability and reliability, implementing new technologies, improving engineering standards and documentation, supporting security and automation, and participating in a 24/7 on-call rotation. The role also involves mentoring engineers and applying AI tools to improve team productivity.

Administer and harden production Linux systems, troubleshoot complex infrastructure and service issues, manage operating system lifecycles, and develop automation using Bash, Python, and Ansible. The role includes configuration management, security updates, monitoring, backups, recovery testing, incident ownership, documentation, controlled changes, and shared on-call support across physical and virtualized environments.

Manage and optimize large-scale Oracle E-Business Suite R12.2 and Oracle database environments across on-premises and cloud infrastructure. Responsibilities include installation, cloning, patching, upgrades, monitoring, performance tuning, backup and recovery, disaster recovery, cloud migration, security compliance, and capacity planning. The role supports Oracle RAC, ASM, RMAN, Data Guard, OCI, Exadata, and DoD environments while collaborating with government stakeholders and engineering teams. It also includes mentoring junior DBAs and maintaining technical documentation.

Develop and maintain backend and frontend services for a SaaS cyber-physical systems platform using Python, Go, React, and cloud-native technologies. Investigate production incidents, replicate customer issues in staging, perform root cause analysis, and deliver feature enhancements. Ensure compliance with FedRAMP, CMMC, and GovCloud requirements while collaborating with distributed engineering teams across the US and Israel.

Build and operate Linux and Kubernetes infrastructure for Azure Front Door’s globally distributed edge fleet. Design, implement, validate, and deploy platform capabilities; optimize performance, reliability, scalability, security, and operations; investigate production issues; develop automation and engineering tools; and provide technical guidance through reviews and knowledge sharing.

Develop and maintain Intelerad’s Unified Storage Layer and cloud storage services for medical imaging applications. Responsibilities include designing software features, translating support and stakeholder requirements into solutions, debugging cloud infrastructure, managing Kubernetes, Terraform, AWS, and CI/CD environments, reviewing code, documenting systems, and delivering work within an Agile/Scrum team.

Administer and optimize enterprise Windows and Linux infrastructure, virtualization, cloud platforms, networks, storage, security, endpoints, identity systems, and disaster recovery. Monitor performance, resolve infrastructure issues, maintain compliance controls, document systems, support technical escalations, and lead upgrades, migrations, and deployments. The role requires independent management of complex environments and strong communication with technical and non-technical stakeholders.

Build and operate large-scale data acquisition systems, including web crawlers, distributed processing platforms, backend services, data pipelines, and asynchronous workloads. Improve ingestion reliability, scalability, observability, fault tolerance, and data quality. Collaborate with data product and engineering teams to develop data products, design schemas and pipelines, and deliver resilient systems in a remote-first environment.

Administer and optimize Oracle databases across AWS RDS GovCloud and on-premises environments. Responsibilities include upgrades, patching, backup and recovery, performance monitoring, SQL and PL/SQL troubleshooting, database security, Linux and Windows administration, RMF and DISA STIG compliance, audit evidence, vulnerability remediation, release support, and technical documentation. The role requires customer communication, independent problem-solving, and occasional after-hours support and travel.

Build and operate large-scale audio data pipelines for AI model training. The role involves discovering data sources, developing reliable data-processing systems, optimizing quality, throughput, and cost, and collaborating with AI scientists. The engineer will own projects from discovery through implementation and ongoing improvement, using strong backend engineering skills in Python, Bash, and Linux environments.

Build and operate large-scale audio data pipelines for AI model training. Responsibilities include discovering data sources, improving data-processing systems for quality, throughput, and cost, collaborating with AI scientists, and owning engineering projects from discovery through continuous improvement. The role requires strong backend software engineering skills, production systems experience, and proficiency in Python, Bash, and Linux, with preferred experience in web crawling, distributed processing, GCP, Terraform, and audio or ML training data.

Build large-scale data pipelines and processing systems that produce high-quality audio datasets for AI model training. Discover data sources, optimize throughput, quality, and cost, operate production systems, and collaborate with AI scientists on next-generation models. The role requires strong backend software engineering, independent problem-solving, and ownership of projects from discovery through continuous improvement.

Build and operate large-scale data pipelines that collect audio data for AI training. Responsibilities include discovering data sources, optimizing data-processing systems for quality, throughput, and cost, collaborating with AI scientists, and owning engineering projects from discovery through continuous improvement.

Build and operate large-scale audio data pipelines for AI model training. Responsibilities include discovering data sources, developing reliable data-processing systems, optimizing data quality, throughput, and cost, collaborating with AI scientists, and owning engineering projects from discovery through continuous improvement.

Build and operate large-scale data pipelines for AI training datasets, including discovering audio sources, processing data efficiently, optimizing quality and cost, and collaborating with AI scientists. Own engineering projects from discovery through implementation and continuous improvement. The role requires strong backend software engineering, production systems experience, Python and Bash proficiency, and independent problem-solving in ambiguous environments.

Build and operate large-scale audio data pipelines for AI model training. Responsibilities include discovering data sources, developing reliable data-processing systems, optimizing quality, throughput, and cost, and collaborating with AI scientists. The role owns engineering projects end to end and requires strong backend software engineering skills, Python and Bash proficiency, production systems experience, and independent problem-solving.

Build and optimize petabyte-scale data pipelines for AI model training, including audio data acquisition, large-scale processing, and dataset quality improvements. Collaborate with AI scientists, operate production systems, and independently manage engineering projects from discovery through continuous improvement. The role emphasizes backend software engineering, scalability, throughput, cost efficiency, and reliable infrastructure.

Build and operate large-scale audio data pipelines for AI model training. Responsibilities include discovering data sources, developing data-processing systems, optimizing quality, throughput, and cost, collaborating with AI scientists, and owning engineering projects from discovery through continuous improvement. The role requires strong backend software engineering skills, production systems experience, and independent problem-solving in an ambiguous environment.

Build and operate large-scale audio data pipelines for AI model training. Responsibilities include discovering data sources, developing reliable data-processing systems, optimizing quality, throughput, and cost, collaborating with AI scientists, and owning engineering projects from discovery through continuous improvement.

Build and operate large-scale AI training data pipelines, discover audio data sources, optimize data processing for quality, throughput, and cost, and collaborate with AI scientists on next-generation models. The role requires strong backend software engineering, production systems experience, and independent ownership of engineering projects in ambiguous environments.

Build and operate large-scale data pipelines for AI model training, including discovering audio data sources, optimizing data processing for quality, throughput, and cost, and collaborating with AI scientists. Own engineering projects from discovery through implementation and continuous improvement. The role requires strong backend software engineering skills, production systems experience, and independent problem-solving in ambiguous environments.

Build and operate large-scale data pipelines for AI model training, including discovering audio data sources, optimizing processing systems for quality, throughput, and cost, and collaborating with AI scientists. The role requires ownership of engineering projects from discovery through continuous improvement, with a focus on production systems, scalable data processing, and efficient dataset development.

Build and operate large-scale data pipelines that collect, process, and optimize audio datasets for AI model training. The role involves discovering data sources, improving data quality, throughput, and cost efficiency, partnering with AI scientists, and owning engineering projects end to end. Strong backend software engineering, Python, Bash, Linux, production systems, and data-processing experience are required, with GCP, Terraform, web crawling, and audio or ML dataset familiarity preferred.

Build and operate large-scale AI data pipelines, discover audio data sources, optimize data-processing systems for quality, throughput, and cost, and collaborate with AI scientists on training datasets. The role requires backend software engineering, production systems experience, independent problem-solving, and ownership of projects from discovery through continuous improvement. Nice-to-have experience includes web crawling, distributed processing, cloud infrastructure, Terraform, dataset quality, and audio processing.