Machine Learning Jobs in Richardson, TX

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 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 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 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.

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 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.

The Senior Lead Machine Learning Engineer will design, implement, and scale ML applications, ensuring performance and high availability while collaborating with cross-functional teams.

Lead design and productionization of advanced ML systems (NLP, speech, recommendations, IR, and agentic/Generative AI). Build and fine-tune LLM/SLM solutions (RAG, tool-calling agents), apply RL/RLHF and PEFT techniques, ensure scalable, secure, and measurable deployments, and collaborate cross-functionally to drive product impact.

Lead end-to-end development and production deployment of causal and predictive ML solutions for private banking. Frame causal problems, build uplift/HTE models and observational causal studies, drive MLOps, monitor model quality, mentor data scientists, and translate causal/LLM research into production in partnership with risk, compliance, and business stakeholders.

Lead development of state-of-the-art ML and deep learning solutions for NLP, speech analytics, time series, reinforcement learning and recommendation systems. Collaborate with business, product, legal, compliance and engineering teams to deploy scalable models into production, design experiments and metrics, and coach AI/ML team members.

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.

Own the end-to-end MLOps lifecycle: turn experiments into reliable production models by building scalable infrastructure, CI/CD pipelines, experiment tracking, versioning, drift detection, production monitoring, and managing cloud and GPU resources while collaborating with data scientists.

Leads USAA’s enterprise Concentration of Risk framework, using actuarial, catastrophe, climate, statistical, and predictive analytics to identify risk concentrations and guide pricing, underwriting, growth, profitability, and capital decisions. Partners with cross-functional teams and executives, develops risk metrics and mitigation strategies, governs enterprise reporting, manages vendors, and communicates complex recommendations to senior stakeholders.

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.

Design, develop, and troubleshoot secure, scalable production code and ML platforms. Drive enterprise adoption of AI-assisted engineering practices, automate remediation, evaluate vendor solutions, apply SDLC/CI-CD, and ensure responsible AI, data sensitivity, resiliency, and operational stability.

Design, develop, deploy, and maintain secure, scalable ML platforms and application code. Drive adoption and validation of enterprise-authorized AI-assisted development practices, automate remediation and operational stability, lead vendor evaluations, and coach teams on responsible AI, resiliency, and secure SDLC practices.