Top Remote Python Jobs in Washington, DC

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.

Build scalable AI training-data pipelines and large-scale data-processing systems. Discover and acquire audio data, optimize data quality, throughput, and cost, and collaborate with AI scientists on next-generation models. Own engineering projects from discovery through continuous improvement in a distributed environment.

Build and operate large-scale audio data pipelines for AI model training. Responsibilities include discovering audio data sources, developing reliable ingestion and processing systems, optimizing data quality, throughput, and cost, and collaborating with AI scientists on training datasets. The role requires ownership of engineering projects, strong backend development, production systems experience, and independent problem-solving in an ambiguous environment.

Build and operate large-scale data pipelines that acquire, process, and optimize audio datasets for AI model training. Responsibilities include discovering data sources, improving data quality, throughput, and cost, collaborating with AI scientists, and owning engineering projects from discovery through continuous improvement. The role requires strong backend engineering, production systems experience, and independent problem-solving in a distributed environment.

Build and operate large-scale audio data pipelines for AI model training. Responsibilities include discovering data sources, developing reliable ingestion and processing systems, optimizing data quality, throughput, and cost, and collaborating with AI scientists. The role requires ownership of engineering projects from discovery through continuous improvement in a distributed, ambiguous environment.

Build and operate large-scale audio data pipelines for AI model training. Responsibilities include discovering audio data sources, developing reliable data-processing systems, optimizing quality, throughput, and cost, and collaborating with AI scientists. The role requires ownership of engineering projects from discovery through continuous improvement, with emphasis on backend development, production systems, data acquisition, and scalable infrastructure.

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. Own engineering projects from discovery through continuous improvement while working independently in an ambiguous environment.

Build and operate large-scale audio data pipelines for AI model training. Responsibilities include discovering data sources, developing reliable processing systems, optimizing quality, throughput, and cost, and collaborating with AI scientists. The role requires strong backend engineering, production systems experience, Python and Bash proficiency, and independent ownership of projects. Experience with web crawling, distributed processing, GCP, Terraform, machine-learning datasets, or audio processing is advantageous.

Build scalable data pipelines and processing systems for AI model training at petabyte scale. Responsibilities include discovering audio data sources, improving dataset quality and processing efficiency, optimizing 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. The role involves discovering data sources, optimizing data processing for quality, throughput, and cost, partnering with AI scientists, and owning engineering projects from discovery through continuous improvement.

Owns end-to-end post-sales technical support for federal and special-program customers in secure, classified, and air-gapped environments. Diagnoses issues across Linux, compute, GPUs, networking, storage, cloud, and applications; collects and sanitizes diagnostics; manages escalations; maintains hardened deployments; supports hardware replacement; writes knowledge-base content; and provides customer and pre-sales support. The role includes on-call rotations, secure-facility work, and regional and domestic travel.

Build and evolve scalable full-stack systems for People Data Labs’ APIs, self-serve products, and customer success tools. Responsibilities include designing frontend and backend architecture, developing React and Python applications, ensuring data integrity, implementing reliable APIs, optimizing performance, operating production services, collaborating with stakeholders, writing technical designs, mentoring engineers, and improving development practices.

Lead the technical direction and architecture of the Foundations platform team, owning authentication, authorization, SSO, secrets management, tenancy, and core platform services. Design APIs, drive services through launch, make technical decisions, establish engineering standards, and mentor engineers without formal management authority. Partner with engineering and product leaders to align execution with priorities.

Leads and grows the Payments engineering team while remaining hands-on in software development. Designs scalable backend systems using .NET, databases, messaging, Docker, and AWS. Manages engineering and QA professionals, mentors team members, prioritizes initiatives, and collaborates with product and business stakeholders. The role combines approximately 60% engineering with 40% people and project management.

Build and operate the platform supporting Honeydew’s digital dermatology product. Responsibilities include migrating data infrastructure to RDS, strengthening AWS security and compliance, standardizing logging, developing AI-assisted vulnerability remediation workflows, creating infrastructure and automation, supporting feature teams, and reviewing code. The role requires backend service and API development, platform security, infrastructure as code, CI/CD, and strong data and architecture skills.

Design and maintain automated testing frameworks for web, iOS, and Android fitness applications. Integrate tests into CI/CD pipelines, build API coverage, test GPS and location features through simulation and sensor mocking, and use AI tools to improve QA workflows. Perform targeted exploratory testing, document defects, collaborate cross-functionally, estimate QA work, and advocate for quality throughout development.

Build and scale full-stack software for an AI-native cybersecurity platform. Responsibilities include developing backend services and APIs, creating frontend applications, collaborating on product features, writing tested code, participating in reviews, troubleshooting across the stack, and improving engineering processes. The role requires 3–5 years of software engineering experience and experience with backend, frontend, databases, and cloud technologies. Docker, Kubernetes, CI/CD, AI/ML, and startup experience are preferred.

Design, develop, deploy, and maintain scalable backend services, APIs, and frontend applications. Own projects from architecture through monitoring, collaborate with product and design teams, participate in technical and code reviews, troubleshoot complex issues, and improve engineering practices. The role focuses on secure, reliable systems across cybersecurity, AI, and distributed systems while providing technical guidance to peers.

Designs and manages cloud infrastructure across development, staging, and production using Terraform and AWS. Builds containerized environments with Docker, ECS, and Kubernetes; develops CI/CD pipelines; manages networking, routing, APIs, WAF, monitoring, and autoscaling. Troubleshoots infrastructure and deployment issues, optimizes performance and reliability, and collaborates with development and QA teams. The role also supports cloud-agnostic architecture, observability, Linux administration, and automation scripting.

Build and maintain high-performance, resilient SDKs across multiple programming languages and platforms. Develop features, improve reliability, collaborate with engineering and product teams, maintain open-source repositories, and influence technical and product decisions. The role requires strong software engineering fundamentals, polyglot development ability, library-building experience, production-quality coding, and expertise in unit, integration, load testing, and benchmarking.

Develop and maintain enterprise Ansible automation, infrastructure-as-code solutions, REST APIs, scripts, CI/CD pipelines, and integrations across complex infrastructure environments. Manage automation projects through the full software development lifecycle, support production systems, document solutions, troubleshoot issues, and collaborate with infrastructure, security, and service teams. The role requires secure coding, Agile delivery, on-call participation, U.S. citizenship, and eligibility for Public Trust clearance.

Develops high-quality software and AI-powered prototypes for national security missions. Responsibilities include architectural design, feature development, automated testing, backlog grooming, sprint execution, API and SDK development, integration debugging, and research involving LLMs, embeddings, and smaller trained models. The engineer independently leads focused efforts while collaborating with product and engineering teams, using modern DevSecOps and CI/CD practices.

Paid, remote 10-week internship supporting software feature development, testing, debugging, code reviews, documentation, API work, deployment pipelines, and sprint activities. Interns collaborate with Engineering, Product, and Design teams while gaining exposure to frontend, backend, cloud infrastructure, DevOps, and platform engineering. The program runs June 7 through August 13, 2027, requires full-time U.S. work authorization and residency, and includes a two-day in-person experience at Amplify headquarters in Brooklyn.

Lead the evolution of Azure infrastructure toward Kubernetes-based, microservices-oriented systems. Design reliable and scalable cloud infrastructure, observability, networking, CI/CD pipelines, and Infrastructure-as-Code. Drive incident response, cost optimization, capacity planning, performance improvements, and automation while partnering with engineering and data teams. Document best practices, mentor colleagues, and support platform strategy in a healthcare environment with PHI, access control, and audit requirements.

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.