Top Senior Machine Learning Jobs in Houston, TX

The Engagement Manager manages project execution, collaborates with technical teams on AI solutions, develops client relationships, and coaches consultants.

The Engagement Manager leads client projects, manages teams, ensures timely delivery, develops client relationships, identifies business opportunities, and provides thought leadership.

The Director leads AI and emerging technology risk consulting, advising on governance, ethical use, and developing risk frameworks, while managing client engagements and sales strategies.

The Staff Data Scientist will lead the development and deployment of ML and GenAI systems to detect billing errors and fraud in healthcare payments, collaborating with cross-functional teams.

Lead product ownership for Advisor Experience, owning roadmap from discovery to delivery. Build integrations and AI features (Aspen AI, client portal), deliver advisor capacity and transition tools, work with engineering/design/data and external partners, and drive Client Excellence KPIs (Net Revenue Retention, NPS, advisor retention, transition success).

Lead and develop a team of five East Coast Enterprise Sales Leaders to drive new logo acquisition and account expansion. Build sales plans, manage pipeline from lead generation through close, recruit and coach leaders, and contribute to GTM strategy while reporting to the VP of Enterprise Sales. Remote-first role with occasional off-hours work.

Lead and manage actuarial projects for healthcare clients, provide technical and strategic actuarial advice, build financial models, mentor junior actuaries, and support practice development and client relationships to drive financial decision-making.

Drive technical wins for enterprise financial institutions by scoping solutions, leading discovery (MEDDPICC), tailoring demos, building business cases, owning RFI/RFPs and POCs, and coordinating product/engineering to validate feasibility for fraud and financial crime prevention.

Lead product vision and roadmap for Tekion's Automotive CRM, translate dealer and partner needs into product requirements, define AI/LLM-enabled CRM capabilities, collaborate cross-functionally to deliver SaaS CRM features, create product artifacts, measure success metrics, and prioritize initiatives to drive adoption and strategic account success.

Build and maintain ML and generative AI tooling, training and inference platform services, model evaluation and testing frameworks, and APIs to expose ML products. Deploy and manage applications across the AI/ML SDLC, mentor teammates, and write production-quality code daily.

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.

Design and deploy agentic AI systems, build ML pipelines, optimize production models, and lead enterprise data standardization and governance initiatives. The role collaborates across business and technical teams to identify AI use cases, establish data quality and metadata standards, promote data literacy, advise on AI best practices, and ensure scalable, reliable, compliant AI solutions.

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

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 role involves leading strategic AI partnerships, driving revenue growth, and managing cross-functional teams while advocating for AI initiatives internally and externally.

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.

Act as the technical lead in enterprise sales: run technical discovery, design and deliver customized demos and POCs, assess API/cloud/model fit, address technical objections, support contract negotiations, and relay product feedback to engineering while staying current on AI/ML trends.

Lead design, build, and operation of data architecture, pipelines, and semantic layers. Manage sprint planning, DevOps metrics, data quality and monitoring, and MLOps enablement. Partner with stakeholders, coach engineers, track project plans, and ensure data availability, accuracy, and documentation.

Lead strategy and build 0->1 AI products that streamline complex healthcare workflows across providers, pharmacies, patients, and pharma partners. Own roadmap, requirements, metrics, and product performance; partner with engineering, data, clinical, and commercial teams to launch and iterate AI-enabled solutions that drive adoption and measurable business and patient outcomes.

Develop scalable machine learning services for data enrichment, managing the ML lifecycle from model training to deployment, and ensuring performance and reliability standards.

Lead backend architecture and implementation for scalable, reliable cloud-hosted web products. Mentor engineers, set standards, integrate agentic AI coding tools, design systems applying ML to large datasets, and balance customer-facing features with technical debt and performance.

Lead end-to-end product areas that improve build speed, cost accuracy, margin, and throughput. Own strategy, roadmap, and execution for complex financial and operational data products, integrate AI/ML into workflows, write precise requirements, instrument metrics, run experiments, and drive cross-functional outcomes with construction, finance, sales, design, and engineering teams.

Lead the Contracts product roadmap for a B2B healthcare SaaS platform, applying deep reimbursement and managed care expertise. Partner with AI engineers to build contract-understanding models, drive adoption, iterate on workflows, create business cases, and coordinate cross-functionally with Legal, Finance, Sales, and SMEs to ensure compliant, revenue-aligned contract execution.

Lead engineering team to define and execute threat detection, platform, analytics, and automation roadmaps. Advance automated, AI-augmented detection and response across SIEM/EDR/NDR/SOAR and cloud environments, partner with Incident Response, and drive measurable risk reduction via metrics and team development.

Embed with customers to discover needs, design and implement production-grade integrations (APIs, data pipelines, event-driven workflows), deploy and troubleshoot solutions, train IT teams, and drive adoption and measurable business outcomes.