Top Software Engineer Jobs in Dallas-Fort Worth, TX

As a Senior Principal Engineer, you'll define and drive the technical strategy for customer experience platforms, lead architectural decisions, and mentor engineering teams to enhance customer and agent experiences.

Own commercial navigation features end to end across mobile, backend, and web surfaces. Build reliable, customer-facing navigation experiences for commercial fleets despite poor connectivity, dynamic schedules, and changing routes. Define product priorities and success metrics, ship maintainable and tested code, improve observability, and collaborate with product, design, Maps, Telematics, and adjacent engineering teams. The role requires strong customer focus, technical depth, and the ability to make practical tradeoffs in ambiguous problem spaces.

Design, build, and operate scalable distributed systems for real-time restaurant payment and financial workflows. Responsibilities include architecting reliable services, protecting sensitive financial data, improving observability and production performance, debugging distributed systems, participating in on-call rotations, applying AI-assisted engineering tools, collaborating cross-functionally, and mentoring engineers.

Lead reliability, scalability, and operational excellence of large-scale database platforms across cloud and on-prem. Build automation-first database infrastructure (Kubernetes operators, IaC, GitOps), drive monitoring/SLOs, incident leadership, performance and cost optimization, and partner with application teams on safe schema/migration practices. Mentor engineers and evaluate AI-assisted workflows to improve productivity and reliability.

Design, build, and operate foundational developer infrastructure across CI, build systems, deployment orchestration, and test platforms. Improve build speed, deployment safety, and test reliability while owning services end to end, including SLOs, observability, and on-call responsibilities. Partner with engineering teams to address platform pain points and lead cross-cutting technical projects through production.

Build and operate Go-based automation for PostgreSQL provisioning, configuration, upgrades, failover, scaling, and decommissioning at GitLab.com scale. Develop a PostgreSQL-as-a-service platform, integrate cloud infrastructure and infrastructure-as-code tooling, improve database performance and reliability, and create safe upgrade automation. The role includes production ownership, architecture leadership, code reviews, documentation, cross-team collaboration, mentorship, and using AI agents in engineering workflows.

Leads a 2–4 person engineering team while remaining hands-on in a TypeScript codebase. Owns delivery planning, performance management, career development, technical contributions, incident response, team health, quality, and compliance. Partners with Product, Design, and engineering teams to translate strategy into reliable software, manage risks and dependencies, remove blockers, and improve productivity through AI-enabled engineering practices.

Lead Apollo’s Rep Experience engineering team building prospecting, account management, task orchestration, dashboards, and AI-driven recommendations. Responsibilities include technical decision-making, architecture, planning, coding mentorship, hiring, people management, on-call sustainability, estimation, process improvement, and cross-functional collaboration with Product, Design, GTM, and engineering leadership.

Lead technical strategy and execution for multi-quarter initiatives, build and maintain software systems using AI, mentor and elevate engineers, collaborate across disciplines, and protect user privacy and security. The role involves working in an AWS and Kubernetes environment with technologies including Python, TypeScript, React, Go, Airflow, dbt, and Claude Code.

Design, build, and operate high-throughput, low-latency ad-serving infrastructure processing millions of requests per second. Optimize GPU-powered TensorRT inference, feature stores, model serving, benchmarking, experimentation tooling, and system reliability. Partner with machine learning engineers to productionize models and own systems through design, deployment, observability, capacity planning, incident response, and optimization.