Top Machine Learning Jobs in Houston, TX

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

Lead enterprise infrastructure and service delivery teams for global networks, data centers, cloud services, and service desk. Own operations, budgets, vendor relationships, ITSM governance, CAB decisions, and drive continual improvement, security, and strategic roadmaps across onshore and offshore assets.

Lead development and delivery of customized ML/AI analytics for power plants and process industries: design, validate, integrate analytics, ensure data quality, translate customer needs to technical solutions, participate in RCM/FMEA, mentor teams, and support deployment across cloud and on-prem environments while maintaining customer relationships and project execution.

The Engineering Manager, Data Science will lead a team to drive machine learning initiatives in AdTech, focusing on optimization and modeling strategies to enhance client success and operational efficiency.

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 role involves designing, building, and deploying computer vision models for vehicle damage detection, covering the entire ML lifecycle and collaborating across teams.

Design, implement, and scale event-driven, distributed backend systems in Python on AWS to process and enrich large-scale document and media datasets. Lead technical projects, mentor engineers, integrate ML/LLM components, optimize performance, maintain observability, and collaborate with Data Science, ML Engineering, and Product to deliver reliable high-throughput content processing services.

Lead zero-to-one product initiatives to transform knowledge discovery using ML/LLMs. Define vision, roadmap, and success metrics; prioritize trade-offs; partner with Design, Engineering, ML, and Data Science; deliver incremental wins while building an extensible platform and improving prospect and subscriber experiences across devices.

Design, build, and optimize event-driven, distributed backend systems and APIs in Python for large-scale content metadata extraction and enrichment. Deploy and operate resilient AWS-based pipelines, collaborate with ML teams to integrate models, and ensure system performance, observability, and data integrity.

Design, build, and scale event-driven, distributed backend systems in Python to extract and enrich metadata at massive scale. Lead architecture, mentor engineers, integrate ML/LLM components, optimize performance, and ensure observability and reliability across AWS-based pipelines.

Lead quota-carrying strategic sales engagements with Global 1000 customers: develop high-value AI use cases and roadmaps, drive digital transformation, manage strategic account plans, collaborate across sales, engineering, product, and marketing, and travel to customer sites up to 50% to close and ensure customer success.

Develop and deploy production-grade quantitative and optimization models for BESS dispatch, renewables, gas generation, and power market forecasting. Build CWENQ platform, data pipelines, ML/DL forecasting, MIP optimization, perform mark-to-market and underwriting analysis, and provide analytical insights for trading, origination, asset management, and risk.

Lead enterprise-wide operational modernization and technology transformation initiatives, managing cloud technologies, analytics, and cross-functional stakeholder coordination.

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

Lead pre-sales engagements by demonstrating Snowflake value, running proofs of concept, designing solutions, partnering with sales and product teams, and closing large enterprise deals while advising on data architecture and AI/ML deployments.

Support development of pricing arrangements, financial models, budgets and AFAs for a law firm. Produce pricing analyses, competitive market research, and performance metrics. Collaborate with attorneys, IT, and data teams to implement pricing tools (Elite 3E, Iridium), leverage AI/analytics for forecasting, and train stakeholders on pricing technology.

Develop and optimize machine learning models for data extraction from tax documents, collaborate on software integration, and mentor junior team members.

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

Develop large-scale Foundation Models for Autonomous Driving & Robotics, collaborating with teams, optimizing model performance, and mentoring junior members.

The Senior Machine Learning Engineer will develop ML APIs, build data pipelines, and collaborate across teams to enhance ML models and applications.

The Director of Master Data Management leads data governance and strategy, ensuring data integrity and consistency across business operations, while managing a cross-functional team.

Build and operate ML training and serving infrastructure by designing, developing, and launching backend systems. Collaborate across teams to decompose projects, support operations and on-call, create monitoring and metrics, perform code reviews, and contribute to developer velocity and platform reliability.

Design and build machine learning systems for Coinbase, responsibly use generative AI tools and copilots, apply human-in-the-loop practices, and deliver measurable efficiency, cost, and quality improvements while collaborating in a remote-first environment with periodic in-person surges.

Lead a cross-functional team to define and execute the product roadmap for Capacity Management and AI Virtual Agents. Own product lifecycle from research through launch, set KPIs for revenue and adoption, drive integrations via APIs, and apply ML/AI/IoT opportunities. Collaborate with R&D, go-to-market, and strategic partners to improve product-market fit and expand addressable market while resolving escalations and optimizing customer outcomes.

Lead and operate a 10,000 sq ft micro-fulfillment lab to validate and scale warehouse automation, robotics, and AI-driven operations. Own strategy, budgeting, vendor selection, experimentation, and deployment frameworks to drive measurable productivity, capacity, accuracy, and cost improvements across Stord's fulfillment network. Build and mentor a multidisciplinary team and partner with internal and external stakeholders to commercialize solutions.