Machine Learning Jobs in Irving, TX

Design, build, test, certify, and deploy autonomy components across perception, planning, control, and learning for large off-road machines. Own robotics components from prototype through production hardware, contribute across the autonomy stack, and support simulation, on-vehicle deployment, and real-world field operations.

Lead risk analytics for merchant services by assessing credit and fraud risk, developing scoring and machine learning models, identifying emerging trends, and producing governed reporting and insights. Review data, forecasting, and programming models; resolve production and modeling issues; document processes; support regulatory compliance; and collaborate with risk, compliance, technology, and business teams. Lead initiatives, mentor staff, and balance business growth with effective risk mitigation.

Develop autonomous and operator-assisted driving perception systems for off-road machines. Lead perception software development involving sensor fusion, computer vision, machine learning, object detection, tracking, classification, and mapping. Collaborate on architecture, integration, verification, validation, safety, and iterative model training while troubleshooting sensor and machine data. The role requires production-quality C++ and Python development, technical leadership, and up to 10% travel to support a full-time onsite position in Irving, Texas.

Develop autonomous and operator-assisted driving perception systems for off-road machines. Design, implement, optimize, and test sensor fusion, computer vision, object detection, tracking, and classification algorithms. Collaborate on machine learning data analysis, annotation, training, software integration, verification, validation, safety, and production deployment. Troubleshoot perception software using code, logs, sensor data, and machine performance information while participating in Agile development.

Design, develop, test, certify, and deploy autonomy components for large off-road machines across perception, planning, control, and learning. Take robotics solutions from prototype through production on real hardware, contribute across the autonomy stack, and collaborate in outcome-focused engineering pods. The role involves simulation, on-vehicle deployment, field robotics, and potentially machine-learning infrastructure.

Defines data architecture for physical AI and autonomous systems, including data contracts, schemas, metadata, governance, lineage, and traceability. Designs multimodal architectures for sensor, video, image, telemetry, event, simulation, and robotics data. Establishes scalable data patterns for robotics training, synthetic data, world models, and AI development. Leads architectural roadmaps and standards while partnering with simulation, robotics, data engineering, and platform teams.

Leads the design, development, deployment, and optimization of agentic AI, LLM, deep learning, and propensity modeling solutions. Builds models for customer behavior, churn, and lifecycle triggers; manages end-to-end pipelines, monitoring, and continuous improvement. Provides technical mentorship, collaborates with marketers and business partners, and presents model insights and recommendations to senior leadership.

Define and execute the product vision, strategy, roadmap, and priorities for web, mobile, and AI-enabled products. Lead initiatives from discovery through launch and continuous improvement while partnering with engineering, data science, design, security, and business teams. Monitor KPIs, user behavior, product analytics, and model performance to guide decisions. Ensure responsible AI practices addressing privacy, security, data quality, bias, and reliability.

Leads software engineering teams developing modern applications, platforms, and services. Provides architecture and coding expertise, manages budgets and resources, establishes technical learning plans, removes impediments, and improves delivery through metrics and testing. Partners with business operations, communicates strategies to executives, and oversees platform definition, software architecture, tool selection, automation, and regulatory alignment. The role requires 8–10 years of software development experience and expertise across cloud, integration, DevOps, programming, database, testing, and AI-enabled tools.

Own the architecture for autonomous-machine perception systems, including modular machine learning, camera and point-cloud detection, sensor fusion, object tracking, object persistence, and 3D mapping. Define interfaces for robotics, simulation, world modeling, validation, and safety analysis. Collaborate with edge platform, robotics, and simulation teams to convert perception algorithms into reliable autonomy capabilities for construction and off-road environments.

Leads enterprise data architecture strategy and governance for Citi’s fraud domain. Responsibilities include developing data models, flows, standards, and remediation frameworks; architecting scalable data solutions; supporting analytics and AI use cases; partnering with senior stakeholders; and managing data architecture analysts. The role requires extensive financial services experience, fraud-domain expertise, enterprise data architecture capabilities, executive communication, and cross-functional leadership.

Leads enterprise solutions architecture for complex IT programs, designing scalable distributed systems and technology roadmaps. Integrates AI, machine learning, generative AI, and workflow automation into secure enterprise platforms. Provides technical governance, risk assessment, systems integration, cloud architecture, and technical leadership across global teams. Mentors architects and engineers while ensuring solutions meet regulatory, reliability, and operational standards.

Develop and deploy autonomous AI agent workflows, RAG systems, and LLM integrations for financial planning and analytics. Build forecasting models for revenue, EBITDA, and other metrics while ensuring workflows are auditable, explainable, scalable, and compliant with financial data governance standards. The role requires Python-based AI development, LLM orchestration, vector databases, prompt engineering, and translating financial business logic into production AI architectures.

Leads a team developing scalable Java software for counterparty credit risk management. Responsibilities include hands-on architecture and development, technical mentoring, code reviews, cloud-native and AI/ML adoption, project delivery, agile practices, stakeholder communication, and oversight of systems integrating trades, collateral, and market data.

Provides technical leadership and defines end-to-end architecture for Adobe Experience Cloud marketing technology and customer engagement solutions. Designs integrations, data flows, APIs, event-driven services, personalization, customer journey orchestration, and communication systems. Partners with product, engineering, security, compliance, marketing, and business teams to translate requirements into scalable, secure, resilient solutions. Supports architecture governance, prototyping, technical planning, risk management, and implementation-ready delivery in an agile environment.

Leads prepaid debit card fraud risk management, including fraud strategy, controls, loss performance, trend monitoring, root-cause analysis, and mitigation. Manages and develops fraud analyst experts while partnering with product, operations, technology, compliance, payment networks, issuing banks, and regulators. Supports launches, audits, client proposals, escalations, and executive reporting. Promotes responsible use of AI, machine learning, analytics, visualization, and data storytelling to improve fraud outcomes.

Participate in Orion180’s 2027 summer internship program across accounting and finance, claims, customer experience, data science, sales, product, or specialty risk. Interns support meaningful projects, analysis, reporting, customer service, underwriting, product research, or business operations while receiving mentorship and professional development. The role is onsite in Melbourne, Florida; Irving, Texas; or Draper, Utah, with potential consideration for future co-ops and full-time positions.

Design and implement enterprise AI architectures spanning GenAI, machine learning, RAG, agentic workflows, data pipelines, APIs, and production deployments. Build Python prototypes and AI services, define governance and evaluation frameworks, integrate models with enterprise platforms, and guide engineering teams through implementation. The role requires hands-on expertise with LLM applications, orchestration frameworks, model lifecycle management, monitoring, security, and scalable production AI systems, preferably in financial services.

Leads enterprise solutions architecture for complex technology programs, translating business needs into scalable, secure systems. Designs distributed, cloud-native, event-driven architectures and integrates AI, ML, generative AI, and Pega workflow platforms. Develops technology roadmaps, governs architecture, evaluates risk, mentors technical teams, and guides modernization and investment decisions. The role partners with executives, engineering leaders, product teams, and global technology stakeholders to deliver resilient platforms supporting consumer banking operations.

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.

Lead design, development, and deployment of enterprise-scale AI/ML and agentic AI solutions. Architect end-to-end systems using Python and Java, fine-tune and optimize LLMs, implement RAG and prompt engineering, build CI/CD pipelines with containerization and orchestration, integrate SQL/NoSQL databases, enforce Responsible AI and compliance, and mentor teams while driving engineering best practices.

Develop and maintain generative AI applications using LLMs, RAG, prompt engineering, and orchestration frameworks. Build scalable Python APIs and microservices, collaborate with engineering and product teams, and support deployment, monitoring, troubleshooting, and optimization in Azure and AWS environments. Contribute to CI/CD, automated testing, containerization, infrastructure automation, code reviews, documentation, and Agile delivery.

Proactively hunts threats across Verizon’s network by analyzing traffic, logs, packet captures, malware, behavioral data, and threat intelligence. The role develops hunting techniques, investigates incidents, identifies vulnerabilities and logging gaps, creates detection hypotheses and alerts, and provides actionable findings to network and security stakeholders. It also documents processes, mentors peers, evaluates security tools, and communicates evolving network threats. Candidates need substantial networking and relevant cybersecurity experience, with expertise in routing, switching, threat detection, and security analysis.

Develop advanced data science and AI/ML solutions using complex healthcare data. Apply statistical modeling, machine learning, NLP, and LLM techniques, including RAG and prompt optimization. Build production-ready workflows integrating models with APIs, data pipelines, enterprise applications, and cloud platforms. Analyze data quality and model performance, communicate findings to technical and executive audiences, and collaborate across product, engineering, clinical, technology, and business teams.

Designs, develops, deploys, and operationalizes machine learning solutions using Python, PySpark, Azure technologies, and large-scale telemetry datasets. Builds scalable data pipelines, trains and evaluates models, applies MLOps practices, and communicates insights through dashboards and presentations. Collaborates with data engineers, data scientists, architects, product owners, and business stakeholders to deliver enterprise machine learning solutions.