Top Remote Machine Learning Jobs in Houston, TX

Leads engineering for one or more product-domain teams, partnering with Engineering Managers, Product, Data Science, Analytics, and Insurance stakeholders. Responsibilities include setting technical strategy and roadmaps, coaching engineers, driving cross-team initiatives, contributing code, leading incident response and resiliency improvements, and establishing engineering standards. Requires strong Ruby on Rails expertise, Rust familiarity, software leadership experience, and excellent communication skills.

Lead client engagements involving AI and machine learning solutions. Translate customer outcomes into technical direction, define requirements and success measures, review architectures and data pipelines, coordinate engineering delivery, and manage project planning, budgets, forecasts, and workstreams. The role requires technical fluency across APIs, cloud platforms, CI/CD, and ML deployment, plus experience delivering custom software and data solutions for complex, multi-stakeholder customers.

Supports the development, testing, and evaluation of AI/ML models for educational products. Responsibilities include data collection and cleaning, feature engineering, model validation, analyzing outputs, identifying optimization opportunities, documenting findings, preparing technical reports, and collaborating with Data Science, Engineering, and Product teams. The intern also explores applications in personalized learning, content recommendations, adaptive learning, NLP, generative AI, and responsible AI.

Leads complex, cross-functional Payment Integrity programs from strategy through execution. Develops plans, roadmaps, milestones, reporting, and executive communications; manages risks, dependencies, stakeholders, and decision points. Coordinates data, analytics, technology, vendor, and emerging AI initiatives using Agile planning practices. Drives accountability, continuous improvement, and successful delivery across teams without direct authority.

Leads enterprise real-world data strategy at Bristol Myers Squibb, translating R&D and business priorities into data investment, acquisition, evaluation, and evidence-generation strategies. Serves as the team’s AI innovation lead, identifying applications for generative AI, machine learning, and advanced analytics. Evaluates datasets, vendors, partnerships, and emerging technologies; develops executive-ready business cases; and drives cross-functional alignment across strategy, data, governance, customer success, and R&D teams.

Leads development and deployment of statistical, machine learning, and causal inference models for customer decisioning. Designs experiments, estimates treatment effects and incremental value, evaluates targeting performance, and integrates analytical solutions into governed production systems. Partners with business and technology teams, communicates complex findings, and continuously improves models using experimental and operational results.

Leads enterprise technology strategy, architecture, modernization, security, reliability, AI adoption, and execution. The VP will oversee engineering and data organizations, develop technical leaders, establish roadmaps and performance standards, guide cloud and data platform evolution, and connect technology investments to measurable business outcomes. This executive partners with the CEO and cross-functional leaders while managing organizational performance, operational risk, governance, resilience, and technology investments.

Lead the technical strategy and multi-year roadmap for Data Labs, overseeing AI benchmarking systems, data pipelines, and reinforcement-learning environments. Manage engineering managers, forward-deployed engineers, and senior individual contributors while partnering with research, product, commercial, and executive teams. Guide platform investment, evaluation methodology, market positioning, client engagement, organizational growth, and architectural decisions supporting AI training and deployment.

Claims Counsel advises on auto insurance coverage, multi-state tort law, civil procedure, and litigation matters. Responsibilities include drafting coverage opinions, researching insurance laws, supporting claims policies and training, collaborating with legal and claims teams, conducting adjuster training, and drafting form letters and releases.

Own the strategy and lifecycle of machine learning models powering maritime domain awareness products. Translate customer and operational needs into model roadmaps, requirements, metrics, evaluation standards, and release criteria. Collaborate with ML engineers on experimentation, error analysis, and deployment decisions. Lead cross-functional execution from ideation through launch, establish feedback loops for monitoring and retraining, and communicate technical tradeoffs to executives, customers, and partners.

Lead the demand generation function by translating go-to-market priorities into segmented campaigns across email, search, social, content, webinars, and events. Own HubSpot automation, Salesforce integration, lead scoring, funnel analytics, marketing-sourced pipeline, budgets, and agency performance. Collaborate with Sales and Commercial Operations to improve lead handoffs and conversion. Apply AI and automation to increase execution efficiency while coaching team members and building the future demand generation team.

Analyze large datasets, identify meaningful patterns, develop machine learning models, and deliver actionable insights for business decisions. Collaborate with cross-functional teams to understand data needs, communicate findings clearly to stakeholders, and stay current with advances in data science and AI technologies.

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.

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.

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.

Develop and deploy machine learning, AI, causal inference, and statistical solutions for advertising measurement products. Build prototypes, conduct experiments, analyze large-scale data, and advance attribution capabilities from early concepts through production. Collaborate with engineering, product, analysts, and account teams to improve measurement and marketing ROI. Responsibilities include owning measurement products, applying Bayesian and marketing models, writing production code, and communicating methodologies and findings to technical and nontechnical audiences.

Owns product lifecycle management, market analysis, customer feedback, product roadmaps, NPI processes, launches, and revenue initiatives. Uses Generative AI, LLMs, predictive analytics, and AI agents to improve research, backlog prioritization, documentation, and competitive intelligence. Collaborates with technical and business stakeholders, mentors junior employees, supports distribution strategy, delivers product training, and oversees new product development. The role is remote within the United States and requires 8+ years of experience.

Lead the vision, strategy, and evolution of LTK’s creator experience and commerce products. Define creator workflows, identify AI-powered opportunities, analyze behavioral data, establish outcome metrics, and drive experimentation to improve engagement, retention, earnings, and product quality. Collaborate across Product, Engineering, Design, Data, and Creator Success while influencing senior stakeholders and guiding complex, multi-quarter initiatives.

Develop machine learning and statistical models to identify, measure, and manage credit and fraud risk. Partner with stakeholders to translate requirements into scalable solutions, analyze large application and transaction datasets, deploy end-to-end models, and improve risk decision-making. The role requires advanced Python or R and SQL skills, expertise in fraud and underwriting models, model deployment, statistical methods, and mentoring junior data scientists.

Build product enablement for global go-to-market teams across AI Agents, APIs, and Creative products. Responsibilities include preparing launch workshops, competitive training, product certifications, onboarding and everboarding content, hands-on demos, role-play simulations, practice environments, and performance measurement. The role partners with product marketing, engineering, research, and enablement teams while using ElevenLabs’ AI technology to create training experiences.

Develop generative AI solutions using Python, AWS, LLMs, chatbots, prompt and context engineering, RAG, vector databases, and agentic AI frameworks. Build coding prompts and contexts with GitHub Copilot, Claude Code, and Amazon Q Developer to convert business stories into production code and pull requests. Design autonomous and multi-agent workflows, memory and token-management systems, caching strategies, MCPs, and real-time AI decisioning features. Auto-lending experience and A/B testing expertise are advantageous.

Owns CSL Plasma’s target-state business architecture, capability models, value streams, architecture decisions, integration standards, and technology roadmaps. Translates business strategy and regulatory requirements into architecture blueprints across digital products, while governing AI, automation, data, and integration patterns. Partners with product, engineering, enterprise architecture, security, privacy, and compliance teams to drive committed designs, architectural consistency, and migration sequencing across a complex regulated environment.