Top Machine Learning Jobs in Houston, TX

SoftSnow is seeking an AI Ethicist to define ethical guidelines, assess risks, and ensure compliance in AI implementations across various industries.

The Senior Principal Product Manager will lead Rockerbox's measurement and modeling products, ensuring accurate methodologies and partnering with Data Science and Engineering to drive product strategy and execution.

Lead machine learning initiatives, oversee product development, improve execution pace, enhance team reliability, and foster team growth at Hightouch.

Lead vision, strategy, and roadmap for data products powering catalog, taxonomy, recommendations, ads, and AI/ML systems. Partner with data engineering to build data platform, governance, identifiers, and quality metrics. Ship data-driven experiences for retailers, partners, and internal teams and translate data quality into measurable business outcomes.

Provide technical advisory and solution feasibility analysis for enterprise AI initiatives. Evaluate AI engineering approaches, integration complexity, reusable components, and architectural tradeoffs. Support prototyping guidance, vendor capability evaluation, and surface governance, risk, and technical constraints to inform enterprise decisions.

Lead product vision and roadmaps across multiple initiatives for Sift's fraud-prevention platform. Partner with engineering, data science, sales, and marketing to define requirements, write specs, run customer discovery and beta programs, and drive go-to-market and adoption. Balance technical execution with strategic portfolio-level product strategy.

Remote technical recruiter responsible for sourcing and hiring software engineers and niche technical talent for startups. Tasks include sourcing via LinkedIn and job boards, shortlisting, verifying technical fit, researching companies and market trends, developing and executing recruitment strategies, and communicating with candidates and stakeholders. Immediate joiners preferred.

Apply machine learning to large datasets to build predictive models, perform data mining, and develop algorithms. Analyze results, create visualizations, and provide insights to inform business decisions.

The role involves developing and managing MLOps pipelines, deploying machine learning models, collaborating with various teams, and ensuring system reliability in oil and gas operations.

The AI ML Operations Engineer will design and manage machine learning systems from data collection to deployment, ensuring system reliability and optimizing workflows.

Lead architecture, design, development, deployment, and optimization of an agentic AI platform. Partner with product, data, security, and business teams to productionize AI solutions, establish engineering best practices, mentor teams, and drive reusable AI services, automation, and operational improvements to deliver business impact.

Own Cash Application and Deduction Resolution Management products end-to-end. Lead discovery, define requirements, prioritize roadmap, partner with engineering/design/sales, ensure financial accuracy, build ERP integrations, drive launches, and maintain product quality and customer-focused outcomes.

The Principal Consultant will lead strategic data modernization and AI programs for enterprise clients, providing architecture guidance and fostering client relationships. Responsibilities include advising on data platforms, overseeing project delivery, identifying business opportunities, and mentoring teams.

The Director of Product Management will oversee the product lifecycle for AI products, focusing on customer collaboration, rapid prototyping, and product strategy. The role involves leading product teams, driving execution, and shaping the product culture.

The VP of Marketing will lead the marketing ecosystem to drive growth, define competitive advantages, influence product design, and mentor a diverse team towards a mission-driven culture.

The SAP Innovation Architect will design SAP automations, mentor engineers, and drive innovation in AI-driven SAP operations, while ensuring scalability and security in integrations.

As a GTM Engineering Intern, you will automate workflows, design demos, and develop AI-powered tools for market outreach while collaborating with sales and engineering teams.

The Senior Data Scientist will develop scalable ML models, analyze large datasets for insights, and collaborate with teams to enhance business performance through data-driven solutions.

The Customer Success Manager drives the adoption of AI solutions in life sciences by managing implementations, developing customer satisfaction strategies, and serving as a trusted advisor.

Two research fellows will receive a stipend and compute credits to pursue projects in legal AI and automated negotiation. Fellows get expert access, private contract data, and are expected to produce a publishable paper, benchmark, or open-source artifact. Research topics include reward modeling under expert disagreement, negotiation simulation, minimal-diff editing benchmarks, uncertainty/hand-off strategies, or related legal AI problems.

Lead vision, strategy, and roadmap for pricing platforms, owning internal Rating Plan Manager and tooling to speed model deployment, improve workflow reliability and observability, enable self-service experimentation, and support regulatory/state-level pricing needs through cross-functional collaboration.

Lead statistical activities for clinical trials from protocol design through reporting. Provide statistical oversight, collaborate with cross-functional teams and CROs, apply SAS/R (and ML) for analyses, automate processes, and mentor junior statisticians.

Design, develop, and deploy causal inference and predictive models (uplift, synthetic control, DML) for customer loyalty, churn, and LTV. Build foundational data models and scalable features, perform large-scale SQL ETL, operationalize models in cloud, partner with stakeholders, and present insights to drive revenue and loyalty program optimization.

Lead engineering strategy and delivery for customer-facing web and mobile platforms; drive AI-native transformation, increase conversion and speed-to-fund, raise engineering talent and velocity, ensure secure and compliant architectures, partner with product and cross-functional teams to deliver measurable business outcomes.

Lead design, development, and deployment of end-to-end recommendation systems. Build, tune, and evaluate scalable recommendation algorithms, integrate ML into products, lead projects, mentor teams, and communicate solutions to clients and stakeholders while driving innovation in replenishment and supply chain analytics.