Machine Learning Jobs in Plano, TX

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 software engineering for P-8A AAS mission systems: design, integrate, and test real-time algorithms and sensor-control software, mentor engineers, perform system architecture and performance analysis, and support deployment and verification in a defense environment.

Lead cybersecurity architecture for cloud-native enterprise solutions: evaluate controls and new technologies, advise stakeholders, provide technical guidance, promote firmwide SDLC practices, and mentor peers to mitigate risk and adopt strategic solutions.

Nine-week summer analyst program within Operations supporting processing, trade/asset support, reporting, transformation, and data management. Responsibilities include transaction processing, reconciliations, client lifecycle support, data analytics and reporting, risk reporting, process improvement, and creating Tableau dashboards. Program includes training, networking, and may lead to full-time roles.

Lead data ownership and governance for wholesale credit risk. Define data strategies, ensure data quality, metadata and lineage, enable data products, mitigate data risks, and coordinate cross-functional delivery for analytics and reporting.

Perform cyber threat intelligence and research, build automations and AI-assisted workflows, support incident response and remediation, assess supplier cyber posture, design AWS infrastructure, integrate data sources, and communicate risk and mitigation recommendations to stakeholders.

Lead oversight of AI/ML and cybersecurity operational risk across the firm: evaluate technology and cyber practices, identify threats, challenge first-line mitigations, conduct thematic analyses, assess AI/ML use cases, and collaborate with stakeholders to strengthen controls and ensure responsible AI adoption.

Lead an agile engineering team to design, develop, test, and operate secure, scalable production software. Provide technical guidance, review code, drive architectural and product decisions, advocate firmwide tools and best practices, and apply cloud-native and machine-learning expertise to deliver resilient applications.

Design and implement enterprise cybersecurity architecture for cloud-native applications. Embed security across SDLC and CI/CD, build Python automation for monitoring and remediation, evaluate vendors, lead practice communities, and collaborate with risk and compliance to translate regulatory requirements into technical controls while validating AI-assisted security outputs.

Lead architecture for cloud-native payments platforms within CIB. Engage stakeholders, define strategy and roadmaps, design and deliver scalable API-driven solutions, improve operational stability, evaluate vendors, and apply enterprise AI responsibly to accelerate architecture decisioning.

Design, build, and scale production-grade NLP and LLM-based solutions (search, summarization, classification, recommendations). Own end-to-end model lifecycle, partner with product/engineering/operations/risk, improve data quality and feedback loops, monitor models in production, and optimize workflows to reduce agent effort and improve consistency.

Lead architecture for eCommerce and in-store payments, designing cloud-native, AI-enabled, secure, and scalable platforms. Advise cross-functional teams on technology selection, build reusable frameworks and production-quality code, develop multi-year roadmaps, integrate APIs and payments protections (tokenization/vault/encryption), and present solutions to senior leaders.

Build and validate ML, optimization, and statistical models on large structured and unstructured consumer datasets; generate insights and actionable recommendations; communicate results to senior stakeholders; contribute across the full modeling lifecycle and advance team knowledge.

Lead cybersecurity architecture for cloud-native applications within consumer banking: define target state, assess risks, propose mitigations, drive automation, lead vendor evaluations and communities of practice, and communicate solutions to senior stakeholders.

Lead cybersecurity architect responsible for designing and delivering cloud-native security solutions, threat modeling, secure design reviews, automation and CI/CD integration, vendor evaluations, AI-assisted security validation, driving reuse and controls compliance (e.g., NIST 800-53), and advising stakeholders while fostering communities of practice and inclusion.

Lead an agile team to design, build, deploy, and operate cloud-native data and ML platforms. Provide technical guidance, write and review secure production code, drive AI-assisted engineering adoption, migrate legacy big-data apps to cloud, build data pipelines and monitoring/provisioning tools, and influence architecture and automation across the SDLC.

Lead design and delivery of scalable, secure software and agentic AI engineering workflows. Architect AI-enabled SDLC automation, set governance/guardrails, build reusable frameworks, advise cross-functional teams, and influence senior leaders to safely scale agentic customer experiences.

Lead and deliver large-scale technical programs and platforms, align cross-functional teams (product, engineering, design, data science), mitigate technical risk, drive execution and accountability, and deliver data-intensive and cloud-native solutions that meet business goals in a regulated environment.

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.

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.

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.

Lead architecture and delivery of high-throughput Databricks/Spark data pipelines and lakehouse patterns. Drive performance tuning, data governance, CI/CD, cluster strategy, and reusable libraries in Python/Java while promoting secure, AI-assisted engineering practices and mentoring the team.