Top Remote Software Engineer Jobs

Lead delivery and technical execution for an AI-driven pipeline integrity platform spanning data architecture, engineering, data science, application engineering, and QA. Remain hands-on through coding, design reviews, prototyping, debugging, and quality oversight. Manage compliance, security, audit readiness, release planning, hiring, team development, and cross-functional collaboration. Ensure platform outputs are traceable, reproducible, secure, and delivered predictably against phased milestones.

Build, deploy, and support secure, scalable full-stack healthcare payment applications. Own front-end, service, backend, analytics, data pipelines, CI/CD, cloud deployment, observability, performance optimization, production incidents, and compliance. Collaborate with product, data, DevOps, QA, business, and client teams to deliver reliable features and platforms across hybrid cloud and on-premise environments.

The Software Engineering Manager will lead and optimize data transformation projects using AI, collaborate with various teams, and ensure project delivery while balancing technical debt and new features.

Leads software engineering teams delivering enterprise applications and SaaS products. Owns architecture, technical design, product lifecycle, roadmap execution, release quality, security, reliability, and operational readiness. Partners with Product, Architecture, Quality, Cloud, and Support teams, while driving engineering practices, AI-assisted development, process improvements, and customer issue resolution. Builds high-performing teams, coaches managers and engineers, and communicates technical direction, risks, and delivery outcomes to stakeholders.

Build and maintain backend systems, payment integrations, data pipelines, and user experiences for Flex’s partner engineering platform. Design robust, fault-tolerant services that integrate with external dependencies, using Java, Spring, AWS, REST APIs, message queues, and scalable architectures. Collaborate cross-functionally with engineering, product, design, infrastructure, sales, partners, and customer success while mentoring engineers and improving development standards.

Lead Pinterest’s technical strategy and roadmap for scalable big data and AI infrastructure. Build frameworks for compute, job, resource, scheduling, and shuffle management across petabyte-scale datasets. Partner with internal customers, provide company-wide technical leadership, and improve data processing reliability, speed, and efficiency.

Build and operate internal software, platform services, developer tooling, Kubernetes infrastructure, and reproducible Nix environments. Develop Go and Rust automation, improve CI/CD and reliability, support Linux systems, networking, identity, endpoints, and SaaS platforms, and resolve ambiguous technical problems independently. Partner across engineering, security, operations, and business teams while mentoring engineers and shaping technical direction.

Leads and mentors a software engineering team while remaining hands-on with Java and full-stack development. Guides architecture, code reviews, technical decisions, sprint planning, delivery, and stakeholder collaboration. Drives engineering practices across testing, CI/CD, monitoring, microservices, service ownership, and continuous improvement. Builds scalable client-facing systems, reliable REST APIs, and high-performance database solutions using ReactJS, AWS, PostgreSQL, Docker, and related technologies.

Develop and maintain .NET applications while migrating authentication from ADFS to Okta. Analyze customer requirements, implement and test software, use DevOps tools to build and deploy applications, and maintain migration, implementation, testing, and user-story documentation. Collaborate with technical teams, project managers, business owners, customers, and users to deliver technically sound projects on schedule and budget. The role is fully remote and supports Department of Justice applications.

Lead development of high-performance Rust parsers, compilers, automata, indexes, and data structures for structured language-model generation. Optimize decoding-loop performance through profiling and benchmarking, integrate Python bindings and C++/CUDA components into inference engines, collaborate across teams, and maintain production-quality code through testing and CI/CD.