Machine Learning Jobs in Richardson, TX

Lead AI/ML product strategy and vision, conduct discovery and market research, own product backlog and roadmap, drive launches, track success metrics (cost, features, risk, reliability), and collaborate cross-functionally to deliver scalable, resilient products.

Support portfolio credit risk monitoring and analysis for residential mortgages. Provide data-driven reporting, identify and report emerging risks, develop risk outlooks, coordinate mitigation activities, and use quantitative and AI tools to improve processes and dashboards.

The CTO will lead technology modernization efforts, drive innovation in cloud computing and analytics, and oversee the transformation of core insurance platforms to enhance customer experiences and business growth.

The Chief Compliance Officer will drive operational excellence and oversee regulatory compliance while managing investment operations and financial oversight.

The Business Development Specialist will identify sales opportunities, conduct market research, develop business strategies, and build customer relationships to achieve sales targets in data and AI solutions.

Executive leader responsible for driving value from MarTech, data, and AI to modernize P&C marketing. Owns audience strategy, lifecycle design, journey orchestration across channels (email, SMS, calls, onsite), testing and optimization, vendor/third-party risk, and capability roadmaps. Builds and leads teams, partners with centralized MarTech and stakeholders, briefs C-suite, and ensures compliance with regulatory and risk frameworks.

Full‑stack software engineer responsible for designing, coding, unit/integration testing, and delivering scalable services for AI/search platforms. Work includes CI/CD, performance tuning, integrating components, contributing to test suites, performing spikes/proofs‑of‑concept, and collaborating in Agile teams to meet functional, non‑functional, and compliance requirements.

Lead development and execution of the enterprise information risk strategy, independently challenge first-line cybersecurity, report risks to the Board, manage incident response, ensure regulatory compliance (GLBA, SOX, PCI-DSS), assess AI/ML risks, oversee audits and maturity assessments, and drive security awareness, disaster recovery, and business continuity programs across the bank.

Lead evidence-driven product learning and prototyping across VOR, DIoT, and LV solutions. Use telemetry, customer feedback, APIs, SQL, and GenAI to define learning objectives, instrument outcomes, analyze behavior, and convert validated findings into delivery-ready requirements and roadmap recommendations.

Lead systems engineering efforts including requirements engineering, systems integration, V&V, and MBSE. Drive technical planning, risk management, architecture/design development, lifecycle management, and mentor senior engineers to influence project direction and deliver ISR/aviation solutions.

The Lead Data Science Product Manager will lead AI initiatives, engage with executives, design AI solutions, and provide overall product leadership.

Lead development and delivery of Fannie Mae's enterprise AI platform capabilities, balancing innovation with security, governance, and cost controls. Own product vision, roadmaps, and core services (models, tooling, agent runtimes), partner with engineering, security, finance, legal, and business teams, onboard priority use cases, and drive adoption through strong stakeholder engagement and developer experience.

Own one or more data science products end-to-end: gather customer requirements, write technical specs, prioritize features, coordinate stakeholders, drive delivery and adoption. Support data science and engineering tasks as needed and promote product usage across the organization while ensuring successful implementation and value realization.

Provide technical leadership to design, build, and productionize large-scale ML systems; deliver and optimize ML models and data pipelines using distributed and cloud architectures; mentor engineers, evangelize best practices, and collaborate with Product, Architecture, Engineering, and Data Science teams.

The AI Engineer will prototype AI solutions, advise on integration, manage AI initiatives, and collaborate across technology teams, ensuring alignment with business needs. Roles include monitoring AI trends, engaging stakeholders, and conducting knowledge-sharing sessions on best practices.

Lead production of machine learning applications, provide technical expertise, and mentor engineers to drive ML model delivery at scale.

Lead a forward‑deployed AI solutions team to translate enterprise AI capabilities into business outcomes. Partner with executives to co‑design and deliver priority use cases, drive AI productivity tool adoption, establish repeatable AI solution patterns, and feed learnings back into the AI platform and governance. Balance rapid delivery with enterprise rigor while coaching teams and managing stakeholders.

Design and implement full-stack Java solutions; define business processes and architecture; work with cloud platforms (AWS/Azure/VMware), infrastructure orchestration, scripting, configuration management, and big-data technologies; apply machine learning libraries; operate in a consulting environment with regular travel.

Lead relationships with large self-insured employers to deliver healthcare data analytics solutions. Act as primary client contact, translate analytics into actionable recommendations, coordinate cross-functional teams, manage implementations and deliverables, present insights to executives, and identify opportunities to improve client programs and drive business growth.

Lead architecture and build cloud-native, resilient enterprise software and Agentic/Generative AI solutions for Claims Adjudication. Design microservices, APIs, CI/CD and observability; integrate LLMs, vector stores, and orchestration frameworks; ensure AI governance and production reliability while mentoring teams and aligning technical strategy with business outcomes.

The Senior Product Owner leads product development for Mainframe solutions at Broadcom, collaborating with cross-functional teams to deliver features based on customer needs and product roadmaps.

Develop, validate, and improve machine learning models (regression, classification, forecasting, optimization). Prepare data, perform feature engineering, train and evaluate models, and support deployment with Data/ML Engineering. Communicate results, document methods, and collaborate on experiments and cross-functional initiatives.

Design, develop, and deploy scalable machine learning models and data pipelines for production. Collaborate with data scientists, engineers, and stakeholders, optimize model performance and scalability, automate deployments, monitor production systems, and evaluate emerging ML and cloud technologies to drive business outcomes.

Deploy, maintain, and improve production ML workflows and platforms for media measurement and customer analytics on AWS. Build SageMaker jobs and endpoints, orchestrate pipelines with Step Functions, Lambda, and Airflow, manage model artifacts and CI/CD, troubleshoot production issues, and collaborate on platform enhancements and operational best practices.

The Senior Machine Learning Engineer will design and deploy ML models, analyze large datasets using PySpark, and collaborate across teams in a cloud environment.