Top Senior Software Engineer Jobs in Richmond, VA

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.

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.

Build and scale full-stack software for employer and Employee Assistance Program products, including employer portals, patient and provider workflows, eligibility, authorization, and billing. Lead technical projects, design secure distributed systems, mentor engineers, contribute to engineering culture, and use modern technologies and AI-assisted development tools. Collaborate cross-functionally to expand access to mental healthcare.

Leads architecture and hands-on development of scalable backend systems and APIs supporting real-time applications. Owns the engineering lifecycle, writes high-performance code, establishes testing and reliability practices, resolves production issues, guides technical roadmaps, collaborates cross-functionally, and mentors engineers. The role requires expertise in object-oriented backend development, distributed cloud systems, automated testing, system monitoring, and technical leadership.

Design and deliver Android system applications for Toast POS devices, including device policy enforcement, setup, firmware and app updates, background services, and fleet deployment. Lead technical design, cross-functional planning, Android and hardware upgrades, automated testing, production issue investigations, and staged rollouts. Mentor engineers and collaborate with product, hardware, QA, and software teams to build secure, reliable device-management systems.

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.

Build, test, review, deploy, monitor, debug, and maintain full-stack software used in K-12 classrooms. Work primarily with React, TypeScript, Node.js, and AWS while improving complex codebases and service design. Participate in incident response, refactoring, code reviews, pairing, mentorship, and cross-functional collaboration with product, design, and education experts. Use AI tools thoughtfully and prioritize reliable, accessible, and equitable learning experiences.

Build Tailscale’s internal AI-enablement tools, workflows, guardrails, and documentation. Partner with engineering leadership and teams to define the AI roadmap, evaluate coding agents and models, improve adoption, and measure effects on velocity and quality. The role involves developing internal developer-facing automation, teaching and influencing engineers, identifying high-leverage AI opportunities, and promoting safe use of AI-generated code in a distributed engineering organization.

Lead end-to-end development of reliable care-agent tooling and software services integrating contact-center platforms with backend systems. Own architecture, performance, security, production operations, and technical direction while partnering with product, care operations, QA, and engineering teams. Translate operational workflows into scalable solutions, mentor engineers, and improve tools using agent feedback, operational metrics, and AI-assisted development practices.

Lead technical vision and strategy for Affirm’s Cards backend engineering organization. Architect scalable and reliable platforms, guide cross-team initiatives, champion AI adoption and engineering excellence, influence technical roadmaps, and mentor engineers through Staff level. The role requires strong architectural expertise, organizational leadership, project management, communication, and cross-functional collaboration.

Own complex software systems and critical workflows end-to-end while ensuring reliability, performance, security, and maintainability. Lead architectural decisions, mentor engineers, manage incident response, improve system health, and communicate effectively with technical and non-technical stakeholders. The role requires extensive Ruby on Rails, AWS or equivalent cloud infrastructure, Linux production operations, frontend development, relational databases, testing, CI/CD, and observability experience.

Develop and debug production-grade full-stack applications across front-end and back-end systems. Build features, APIs, databases, and data models; troubleshoot end-to-end issues; analyze unfamiliar codebases; translate implementations across programming languages; and review AI-generated and developer-written code for correctness, security, scalability, performance, and maintainability.