Top Machine Learning Jobs in Houston, TX

Win new manufacturing customers for Cognite’s industrial AI and data platform. Responsibilities include territory strategy, prospecting, complex enterprise sales cycles, value-based selling, negotiations, forecasting, CRM updates, customer presentations, and collaboration with presales, product, and marketing teams. The role requires approximately 50% travel and focuses on helping chemical and process manufacturing customers achieve measurable digital transformation outcomes.

Lead agile design and development projects for mobile apps and digital products. Coordinate product, design, engineering, marketing, leadership, and client stakeholders; manage project plans, scope, requirements, backlogs, acceptance criteria, deadlines, workflows, and deliverables. Support scoping and client relationship management while ensuring successful end-to-end software launches and alignment with business objectives.

Leads Utility’s AI practice by defining its roadmap, overseeing AI research and deployment, identifying client opportunities, mentoring AI teams, and establishing responsible AI practices. The role combines technical leadership, product strategy, client advisory, and operational execution across machine learning, generative AI, and intelligent automation initiatives.

Lead AI engineering teams to design, build, and deploy end-to-end AI solutions. Manage client relationships, plan and deliver projects, mentor staff, drive adoption of cloud-native microservices, CI/CD, vector databases and orchestration tools, and ensure high standards of quality, innovation, and compliance.

Leads teams designing and deploying AI-driven enterprise and cloud security solutions. Responsibilities include developing machine learning systems, integrating data infrastructure and pipelines, performing advanced modeling and analysis, and building AI applications with Python, Java, and C++. The role involves client engagement, strategic problem-solving, stakeholder validation, coaching, process innovation, and operational excellence while addressing complex cybersecurity and privacy challenges.

Leverage advanced analytics, data manipulation, visualization, statistical modeling, predictive analytics, and machine learning to generate client insights and recommendations. Apply Adobe Experience Cloud solutions to support business transformation and strategic decision-making. Build client relationships, manage and inspire teams, navigate complex engagements, uphold professional standards, and deliver quality outcomes while developing technical and consulting expertise.

Leads data analysis and advanced analytics workstreams for clients, using statistical modeling, machine learning, data visualization, and Adobe Experience Cloud technologies to improve operations and strategic decision-making. Manages project planning, budgeting, execution, and completion while mentoring teams, maintaining data integrity and security, building Tableau and Power BI dashboards, identifying business opportunities, and integrating innovative analytics practices.

Develop and operate production ML and AI systems powering patient experiences such as matching, ranking, recommendations, onboarding, and personalization. Build reusable ML infrastructure, pipelines, evaluation, observability, and model-serving capabilities. Provide technical leadership across the ML lifecycle, guide architecture, evaluate custom versus foundation-model approaches, and partner with ML and Patient Engineering teams to deliver reliable, scalable AI products.

Leads the AI Engineering team across applied AI features and foundational ML infrastructure. Responsibilities include managing Senior and Staff engineers, hiring and coaching, deploying LLM-powered capabilities, building MLOps and AI observability platforms, balancing product delivery with platform investment, and ensuring privacy, compliance, evaluation rigor, and clinical safety in production systems.

Own the strategic vision and roadmap for Coursera’s AI-driven search experience. Lead conversational search, personalization, semantic and multilingual search, ranking, and discovery improvements. Partner with engineering, design, research, data science, ML, marketing, and partnerships teams to deliver scalable platform capabilities. Use experimentation, market analysis, KPIs, and customer insights to improve conversion, merchandising, learner experience, and revenue.

Set the technical vision for data science, lead complex statistical and machine learning model development from concept through production, and architect scalable modeling platforms and pipelines. Develop credit risk, pricing, and loss forecasting models; establish model governance standards; partner with senior leaders; mentor data scientists; and communicate technical strategy to executives. The role emphasizes production deployment, monitoring, cloud architecture, and high-impact decision-making across the business.

Develops and deploys production machine learning models and data science solutions for business, legal, investigative, and regulatory needs. Designs AI evaluation frameworks assessing bias, accuracy, reliability, and performance; analyzes structured and unstructured data; builds pipelines and testing tools; and translates legal and business requirements into technical solutions. The role collaborates with lawyers, clients, and cross-functional teams, manages concurrent engagements, communicates insights, and supports revenue-generating professional-services delivery.

Own the technical side of deal progression for airline deployments: qualify opportunities, run demos and POCs, author integration and deployment architectures (PSS, APIs, data feeds, DNS, identity, networking), respond to RFPs/security questionnaires, scope SOWs and NFRs, and build reusable demo/reference assets to drive adoption and expansion while liaising between sales, engineering, and customers.

Designs and drives solution architecture strategies for operational facilities, aligning IT and OT solutions with enterprise standards and business objectives. Advises cross-functional delivery teams, ensures scalable and resilient designs, resolves complex system integration challenges, and mentors architecture teams. The role focuses on Microsoft Azure cloud solutions, AI and generative AI, data architecture, integration, security, and emerging technologies supporting mission-critical oil and gas operations.

Designs, develops, deploys, maintains, and optimizes AI and machine learning solutions. The consultant will build NLP and computer vision models, analyze datasets, troubleshoot applications, use Azure data services and Synapse Analytics, and improve model performance. Responsibilities include collaborating with technical teams and business stakeholders, documenting solutions, presenting findings, and occasionally traveling to Middletown, Pennsylvania for orientation, meetings, implementations, or system issues.

Leads new business development, account growth, commercial discussions, client relationships, and sales operations for AI and GenAI data solutions. Partners with engineering, delivery, and customer success teams to understand frontier lab needs, structure projects, close opportunities, and expand client accounts. Requires quota-based technology sales experience, startup experience, STEM education, and strong AI/ML market knowledge.

Support analytics by cleaning, validating, and transforming data; build dashboards and KPI reports; perform trend, cohort, and A/B testing analyses; improve data pipelines and forecasting; present insights to cross-functional teams.

Design, develop, and implement AI/ML models; analyze large datasets; integrate AI solutions into existing systems; apply MavLink for autonomous systems; collaborate across teams and improve ML development processes.

Analyzes supply chain and SAP S/4HANA planning data to improve forecasting, inventory levels, material availability, and planning execution. Develops dashboards, KPI scorecards, reports, and analytics solutions using SAP Analytics Cloud, SAP Fiori, embedded analytics, CDS Views, and Power BI. Collaborates with procurement, production, and logistics teams, monitors data quality, identifies risks and automation opportunities, supports AI initiatives, and trains stakeholders on analytics tools and practices.

Develop and maintain production machine learning models, build scalable training and serving technologies, optimize real-time and batch ML pipelines, and monitor model performance and drift. The role involves deep neural networks, recommendation systems, automated retraining, ML research, engineering excellence, and communicating technical concepts to diverse audiences. Candidates need 6+ years of industry ML experience, strong coding skills, and experience deploying neural networks at scale.

Leads the architecture and delivery of enterprise order orchestration integrations across quoting, order capture, fulfillment, and billing. Owns integration standards, API and event-driven design, technical backlogs, dependencies, governance, compliance, and stakeholder communication. Designs AI-agent capabilities for classification, routing, anomaly detection, and autonomous execution, while coordinating internal teams and external partners. Requires enterprise integration leadership across CRM, CPQ, ERP, and order management platforms, with familiarity in Salesforce, SAP, MuleSoft, IFS Cloud, SOX, and AI automation.

Own audience or measurement products for Fetch’s monetization business, from problem framing through delivery. Build predictive models, datasets, population inferences, and causal measurement studies using receipt, partner, panel, survey, and contextual data. Partner with Product, Engineering, Sales, and GTM to productionize analyses, communicate methods and limitations to customers, and improve products using campaign outcomes, holdouts, and fresh data.

Lead the design, productionization, and operation of machine learning and agentic AI capabilities. Partner with product engineering, shape AI/ML architecture, build evaluation systems and data pipelines, and develop reliable MLOps tooling. Champion responsible AI, safety, latency, cost, and error handling. Serve as a technical resource for engineers, influence direction, mentor others, and occasionally explain AI functionality to customers or customer-facing teams.

Leads a multidisciplinary team of automation and power-generation subject matter experts. Provides technical oversight for controls, generators, protection, telemetry, digital infrastructure, system integration, predictive analytics, and industrial networking. Establishes engineering standards, resolves complex technical issues, supports product development and commissioning, leads root-cause analysis, evaluates technologies, and develops SME capabilities across engineering, operations, manufacturing, and field teams.

Serves as the technical expert for power generation automation, controls, telemetry, digital infrastructure, generator systems, and protection systems. Leads system integration, architecture standardization, complex troubleshooting, root cause analysis, commissioning, technical risk assessment, and lifecycle engineering. Supports predictive analytics, condition monitoring, AI-driven asset performance, remote connectivity, and cloud integration while collaborating with engineering, operations, manufacturing, product service, and field teams.