Top Remote Linux Jobs in Miami, FL

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 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 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 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 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 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 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 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.

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 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 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.

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.

Design and build data infrastructure automation, observability, health tooling, dashboards, and system improvements for large-scale object storage platforms. Lead technical designs and feature rollouts across services, improve fleet stability and throughput, automate hardware lifecycle processes, and partner with on-call teams to reduce operational toil and incidents.

Operate and improve large-scale NVIDIA GPU clusters across bare-metal and rented capacity. Build automation for provisioning, health checks, remediation, monitoring, scheduling, and capacity management. Own Linux images, drivers, CUDA, containers, networking, storage, and Slurm. Diagnose performance and hardware issues, evaluate GPU providers, support ML training workloads, and maintain secure infrastructure. The role also includes hardware installation, datacenter coordination, and vendor management.

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.

As an Android Software Engineer, you'll design and develop applications, enhance Android components, and improve telemetry solutions for device reliability in rugged tech environments.

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.

Own and evolve production infrastructure for AI workloads, including Kubernetes, cloud-native migrations, scheduling, orchestration, observability, infrastructure as code, deployment workflows, security, reliability, and incident recovery. Partner with engineering and business operations teams to build dependable platform capabilities. The role requires deep production Kubernetes experience, strong cloud and Linux fundamentals, architectural judgment, automation skills, and end-to-end ownership of infrastructure systems.