Machine Learning Jobs in Plano, TX

Ten-week, full-time paid Data Science internship (June–August 2027) working on machine learning, deep learning, and large-scale data analysis. Interns evaluate modeling tools, integrate internal and external data/APIs, design data science models, and present insights to stakeholders. In-person attendance at an assigned U.S. location is required; interns may be considered for full-time roles after graduation.

Ten-week, full-time paid Data Science internship for current PhD students. Interns will build and evaluate modeling and analytics tools, integrate internal and external data sources, design data science models, and communicate results. Candidates must continue their PhD after the internship and may be evaluated for full-time roles upon graduation.

Support deployment and integration of software within cloud environments. Use automation, AI-enabled tools, and scripting to install, configure, test, troubleshoot, and document solutions while collaborating with cross-functional teams in labs and production.

Lead enterprise architecture for large-scale software and platforms, evaluate and integrate AI and emerging technologies, develop architecture assessment tools, drive governance, produce secure production code, and provide technical leadership across teams to ensure scalability, resiliency, security, and operational excellence.

Lead and grow multiple product-focused software engineering teams; mentor engineers; collaborate with product managers; improve engineering practices; design, develop, test, deploy and support full‑stack and microservice solutions on AWS; ensure availability, resilience, and scalability; migrate monoliths to microservices and drive adoption of modern technologies and Agile practices.

Senior solutions engineer who designs, builds and supports network automation applications for deployment and administration of Dell SONiC, Cisco ACI and Arista. Develops full-stack services (Python/Java/Node/React) on Kubernetes with IaC, CI/CD and observability; enforces security and code quality, mentors teams, and drives automation and AI-assisted tooling within Agile delivery teams.

Lead and grow multiple product-focused software engineering teams building cloud-native, ultra-low-latency, highly resilient payment systems. Drive architecture, microservices decomposition, AWS deployments, engineering practices, cross-team collaboration, mentorship, and adoption of cutting-edge AI tooling to accelerate development and improve system availability and scalability.

Lead and scale engineering teams to transform the network into an Intent-Based, Infrastructure-as-Code platform. Drive automation, AI integration, microservices, cloud deployments, and engineering practice maturity. Mentor engineers, collaborate with product managers, improve resilience and scalability, and replace manual operations with intelligent network automation.

Lead design-driven technical solutions that blend UX and software development. Create service flows, wireframes, prototypes, and CX strategies; guide technical teams and integrate user research, AI, and accessibility into products.

Lead product management for enterprise product and experience initiatives, partnering with design, data, ML, and engineering to define strategy, prioritize roadmap, prototype solutions, and deliver customer-centric, technology-driven products that achieve business outcomes.

Lead enterprise product and experience initiatives, guiding cross-functional teams (product, data, design, ML, engineering) to define strategy, deliver customer-centric digital products, prototype and apply emerging AI/automation tools, and drive transformational outcomes across Capital One.

Provide legal and regulatory monitoring and analysis for Consumer & Community Banking; draft summaries; manage regulatory and change-management matters; design and improve legal processes; evaluate and apply emerging technologies and AI prompts for legal workflows; respond to regulator, audit, and compliance inquiries; support special research projects and collaborate across teams to solve problems and prioritize work in a fast-paced environment.

Lead enterprise payments architecture for card-present solutions, provide technical governance and guidance, integrate AI-enabled capabilities securely, develop and review production code, evaluate emerging technologies, and drive architecture decisions across product families.

Lead architecture for data, reporting, and analytics product family: set target-state designs, guide technology evaluation and AI-enabled integrations, produce and review production code, enforce governance, resiliency, security, and auditability, and provide technical leadership across teams and vendors.

Lead architecture for global cloud-based eCommerce payment platforms. Advise stakeholders on design and technology selection, define multi-year roadmaps, apply AI-enabled architecture patterns, ensure security/compliance, evaluate vendors, and present strategies to senior leaders to modernize merchant acquiring, payment gateway, and developer-facing APIs.

Lead and deliver advanced analytics and machine learning solutions to support pricing, revenue optimization, and partner relationships. Collaborate with cross-functional teams to design, validate, and implement models at scale using large transaction datasets and modern big-data tooling. Translate technical insights into actionable business recommendations.

Lead architecture strategy and roadmaps for cloud-based, AI-enabled payment systems. Advise on technology selection, build reusable secure engineering patterns, develop and review production code, govern APIs and integration patterns, and drive digital transformation for onboarding and self-service experiences.

Lead customer success for Infrastructure Platform customers: define strategy, drive onboarding, adoption, expansion and retention, coach teams, track health/CSAT metrics, and coordinate cross-functional delivery across cloud platforms for large, complex accounts.

Develop, enhance, and support CAD/EDA tools and flows for NAND, DRAM, and HBM designs; provide production support, documentation, training, and collaborate across design, process, and vendor teams. Apply CAD software engineering and AI/ML techniques to improve automation, productivity, and design quality.

Lead architecture design and governance for trade surveillance products, provide technical direction, integrate enterprise AI securely and auditable into architectures, develop and review production code, and establish reusable SDLC/toolchain patterns.

Lead and mentor an agile engineering team to design, build, test, and operate secure, cloud-native production software. Provide technical direction, deliver high-quality code, drive product design decisions, and advocate firmwide engineering practices while applying AI/ML expertise.

Review and audit machine learning models and model risk practices across the enterprise. Partner with cross-functional teams to design, train, evaluate, validate, and implement ML and NLP solutions, using tools like Python, Spark, H2O, and AWS. Translate technical findings into business recommendations and assurance for stakeholders and the Audit Committee, while researching and applying emerging Generative AI and state-of-the-art methods.

Lead control and risk management activities within Consumer & Community Banking. Partner with stakeholders to identify gaps, design and implement control strategies, use automation/AI/data analytics for monitoring, own Risk & Control Self-Assessments, drive regulatory compliance projects, and recommend improvements using digital risk management and agile practices.

Lead design, productionize, and maintain machine learning systems at scale. Build and optimize ML models, data pipelines, and cloud-based architectures; automate CI/CD, monitor production models, and collaborate with cross-functional teams to ensure responsible, explainable, and high-performance ML deployments.

Lead engineering and ML platform work: design, develop, and maintain secure, scalable production code and ML infrastructure; drive adoption and validation of enterprise AI-assisted development practices; automate remediation, evaluate vendors, and coach teams on responsible AI, resiliency, and security within agile CI/CD workflows.