Top Remote Software Engineer Jobs

Build and maintain backend product features using Python, Django, and relational databases. Develop APIs, asynchronous jobs, integrations, internal tools, and data-processing workflows while using and critically reviewing AI-generated code. Investigate incidents, resolve customer-impacting issues, improve reliability and database performance, and protect data integrity, security, tenant isolation, and auditability. Own work through testing, deployment verification, documentation, and follow-up while collaborating closely on a small engineering team.

Build and operate production Web3 products across backend, frontend, and smart contracts. Develop Go and TypeScript services, React dApps, SDKs, tokenization infrastructure, smart-wallet integrations, and blockchain event processing. Own on-chain/off-chain architecture, security, testing, observability, deployments, and on-call operations. Review code and configure AI-assisted engineering tools for the repository.

Develop and maintain C#/.NET software for electric and gas distribution engineering tools. Implement features, fixes, integrations, tests, and CI/CD automation while configuring enterprise systems for customer needs. Collaborate in Scrum, participate in reviews, reduce technical debt, document processes, and mentor teammates. The role may involve GIS or Enterprise Asset Management integrations and requires strong software development experience in utility or energy infrastructure environments.

Build and own mobile SDKs for identity and fraud prevention products, focusing on Android development, device-level signal collection, authentication, and emerging agentic identity use cases. Partner with product and engineering leaders on architecture and roadmap decisions, improve observability and production resilience, mentor engineers, and drive technical initiatives across Twilio and Stytch.

Lead the design and implementation of scalable distributed systems, modernize monolithic applications, and improve developer experience through CI/CD, automated testing, refactoring, and TDD. Make major architectural decisions, establish engineering standards, and mentor engineers through complex technical challenges. Collaborate with product owners and designers in an agile environment while building and operating reliable software for millions of users.

Build and maintain software services supporting Azure AI capabilities in air-gapped and sovereign cloud environments. Develop deployment automation, release validation, monitoring, and observability tools; collaborate across Azure on networking, identity, and infrastructure dependencies. Own features from design through production, participate in on-call rotations, troubleshoot service issues, write secure and tested code, and contribute to reviews, documentation, and continuous engineering improvements.

Build and maintain infrastructure tooling for thousands of servers and distributed services. Responsibilities include host provisioning, configuration management, rapid code deployment, service monitoring, automated testing, linting, and infrastructure supporting high-volume real-time data pipelines. The role involves end-to-end project ownership, frequent code delivery, debugging complex distributed systems, and collaboration within a high-performing engineering team.

Design and implement GPU performance profiling tools focused on AI applications. Build features used by CUDA developers, collaborate with NVIDIA compiler and CUDA driver teams, and influence tool design. The role requires strong C++ software engineering experience, GPU hardware knowledge, and familiarity with performance tools, with preferred expertise in CUDA kernel programming, profiling, and DWARF parsing.

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.

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.