Machine Learning Jobs in Irving, TX

Design, develop, and integrate real-time simulation and digital twin capabilities using NVIDIA Omniverse and physics engines. Implement simulation logic, validate models, build high-fidelity 3D visualizations, create tooling and test pipelines, and lead/mentor cross-functional teams to deliver accurate, high-performance machine and site simulations.

Lead enterprise observability strategy and engineering for APM, logging, tracing, metrics and telemetry. Manage engineering managers, drive platform modernization, resilience, SRE practices, vendor and budget management, stakeholder influence, and talent development to improve detection, recovery, and operational stability at scale.

Lead design and implementation of automated, API-first vulnerability and patch management solutions across hybrid/multi-cloud environments. Integrate scanning into CI/CD and IaC, build data pipelines and analytics for vulnerability prioritization, support AI-driven decisioning, and provide technical leadership to reduce security risk and improve remediation workflows.

Lead research, design, and implementation of automated, AI-enabled vulnerability and patch management solutions. Build API-driven integrations, data pipelines, and ML prototypes to generate actionable vulnerability intelligence. Partner with engineering and cloud teams to integrate security into CI/CD and IaC, aggregate scanner and cloud data, and provide technical leadership across hybrid and multi-cloud environments.

Lead strategy, roadmap, and delivery of AI-powered and digital growth marketing capabilities. Partner across Product, Data Science, Engineering, Risk, and Marketing to design, test, and scale LLM and personalization solutions, define success metrics and governance, run experimentation, and drive measurable acquisition, engagement, and efficiency outcomes.

Lead solution design by partnering with stakeholders to gather requirements, evaluate technologies, and develop business cases. Build and support applications using low-code, RPA, ML, GIS, and traditional development (.NET, Python, SQL). Drive data modeling and warehouse efforts, vendor selection, and transition projects to IT PM for delivery while providing ongoing support and continuous improvement.

Lead design and implementation of enterprise and cloud security architectures (AWS/Azure/GCP). Develop security strategies, conduct cloud and IAM assessments, recommend tools/processes, and guide deployment and incident response improvements. Advise on frameworks (NIST, ISO, COBIT), zero-trust, SIEM, vulnerability management, and emerging tech risks. Train client teams and drive compliance and remediation.

Serve as the Technical Product Owner on an Agile Scrum team, collaborating with architects, cloud engineers, and developers to maintain and prioritize the product backlog, define scope and roadmaps, craft customer-centric user stories, introduce technical innovations, and measure product performance to achieve business results.

Lead global marketing for Caris AI Insights, defining positioning, messaging, GTM, and commercialization. Drive product launches, sales enablement, thought leadership, customer evidence, competitive analysis, and cross-functional alignment to accelerate adoption of AI-driven oncology intelligence across healthcare, pharma, and research customers.

Build platform-agnostic governance-as-code: intake APIs, automated risk scoring, SDKs, CI/CD-integrated governance checks, registry ingestion, audit artifacts, and runtime guardrails for AI agents.

As a Data Scientist in the Innovation Team, you'll develop machine learning algorithms on molecular sequencing data to enhance cancer diagnostics and treatment, collaborating with diverse experts on high-impact projects.

Lead architecture and development of scalable, secure systems; set engineering standards and CI/CD practices; mentor engineers; drive SRE/DevOps collaboration; adopt AI/ML tools; own incident response, root-cause analysis, performance optimization, IaC and cloud-native improvements; align technical roadmaps with product and business goals.

Serve as a bridge between business and technical teams to identify, design, validate, and support AI, automation, and analytics solutions. Gather and document requirements, translate business needs into AI use cases, support POCs and deployments, monitor model performance and KPIs, ensure responsible AI practices, and drive adoption through training, change management, and continuous optimization.

Lead development and execution of AI-integrated content strategies for financial products. Architect language systems, information architectures, and CMS/AI tooling; ensure content accuracy, brand alignment, ethical standards, and optimized digital engagement. Collaborate with product, design, and engineering teams, apply analytics and UX/journey thinking, and continuously refine AI content capabilities.

The Cybersecurity AI_ML Engineer develops and maintains security systems using AI and machine learning, implements threat detection, analyzes security metrics, and collaborates on security incidents.

Lead Solution Architect provides technical leadership for digital transformation, defines end-to-end Azure/cloud architectures, mentors architects, sets standards and patterns, drives integration and automation, and partners with business and technology leaders to deliver scalable, secure mobile and web solutions.

Develop and execute data-driven marketing strategies that use AI, machine learning, and automation to drive brand awareness, customer acquisition, and revenue. Perform market research, competitive analysis, campaign optimization, CRM and marketing automation integrations, SEO/SEM and paid media optimization, dashboarding, and cross-functional collaboration with sales, IT, and product teams to measure and improve marketing ROI.

Senior Business Systems Analyst to define requirements for agent-facing servicing applications, design integrations/APIs with core banking systems, translate needs into user stories and acceptance criteria, improve agent workflows and productivity, and identify AI/ML opportunities for fraud detection, automation, and operational analytics. Supports testing, validation, and releases.

Lead strategic design, implementation, and scaling of Palantir Foundry/Gotham data solutions. Oversee cross-functional teams, manage full project lifecycle, deliver data integration, governance, analytics, and ML capabilities, and engage stakeholders and clients to translate business needs into scalable Palantir-powered solutions.

The role leads application systems analysis and programming, drives architecture strategy, evaluates technologies, and influences senior stakeholders in the investment domain.

Design, deploy, and maintain analytics and ML models to support energy trading strategies. Conduct market research, data analysis, and reporting. Collaborate with traders, analysts, and IT to improve trading performance and operational efficiency.

Lead proactive, hypothesis-driven threat hunts using SIEM/EDR/log data to identify adversary activity. Design and maintain hunt playbooks, analyze network and system logs, collaborate with SOC/IR/Red Team, present findings to leadership, and mentor other security staff to strengthen detection and response.

Lead design, development, and production deployment of scalable ML systems across domains. Own end-to-end ML lifecycle, architect pipelines and serving, drive MLOps maturity, mentor engineers, conduct design/code reviews, and ensure monitoring, governance, and model reliability in production.

Design, build, and deploy ML models and end-to-end pipelines; implement MLOps practices; deploy containers to cloud production; monitor model performance and drift; collaborate with engineers and product teams; optimize latency and throughput; write production-quality code and mentor junior engineers.

Lead technical vision and architecture for enterprise ML platforms and MLOps. Design training, feature, serving, and observability systems; establish ML lifecycle and governance; solve complex cross-team engineering problems; mentor senior engineers; evaluate emerging AI technologies; drive cost optimization, compliance, and organizational alignment.