Top Machine Learning Jobs in Houston, TX

Own enterprise sales across the TOLA territory by expanding existing accounts, acquiring new logos, managing the full sales cycle, developing territory strategies, forecasting, building channel partnerships, and achieving revenue targets. The role requires executive-level selling, solution-selling expertise, networking and security knowledge, and the ability to communicate AI and technical business value.

Develop and optimize machine learning and statistical models for fraud detection and anti-money laundering. Analyze large datasets to identify fraudulent patterns, build scalable data pipelines and features, evaluate model performance, and collaborate with engineering, product, and operations teams. Communicate insights to technical and non-technical stakeholders while maintaining data privacy and security compliance. The role offers significant ownership in building Oscilar’s early machine learning stack within a remote-first startup.

Own LanceDB’s product direction by shaping the roadmap with technical leadership, researching users and competitors, gathering continuous user insight, and driving cross-functional prioritization. The role partners closely with engineering as a technically respected peer, defines product changes for AI and data-platform users, uses metrics and experiments to guide decisions, and operates with high ownership in an ambiguous environment.

Lead and operate a 24x7 IT and OT Security Operations Center as a player/coach. Manage SOC staff and MDR relationships, oversee detection, investigation, and response across IT and industrial control systems, drive AI/ML and automation adoption, maintain playbooks and metrics, and collaborate with cross‑functional teams to improve security posture.

Lead the architecture and implementation of AWS cloud-native platforms for fraud detection, risk analysis, and digital intelligence. Design scalable, resilient, secure, low-latency REST and event-driven systems using Java, Kubernetes, Kafka, and SQL/NoSQL databases. Set technical strategy and architecture standards, mentor engineers, conduct reviews, and guide multiple teams. Partner with product, data science, security, and business leaders to embed AI and machine learning capabilities, while improving delivery, observability, compliance, and cost efficiency.

Advise enterprise clients on AI and data strategies, transformation roadmaps, operating models, governance, privacy, technology rationalization, and business cases. Lead discovery workshops, executive engagements, and art-of-the-possible sessions; prioritize use cases and connect technical recommendations to business outcomes. Build the advisory practice, develop offerings, manage multidisciplinary teams, mentor staff, strengthen client and partner relationships, and support successful distributed project delivery.

Tune AML and fraud detection models using customers’ live transaction data. Investigate missed cases, alert quality, pattern drift, and false positives; validate changes through backtesting and sample review; document regulatory rationale; and present model performance and business value to compliance, risk, and data science stakeholders. Partner with customer teams on account strategy and relay customer-specific or systemic findings to product and model functions.

Drive net-new enterprise customer acquisition from lead generation through contract closure. Build C-level relationships, develop account strategies, manage CRM pipelines and forecasts, negotiate complex contracts, and close large AI, machine learning, and data talent engagements. Partner with marketing, customer success, and talent teams to deliver customer outcomes and expand Factored AI’s enterprise footprint.

Own the production lifecycle for machine learning and LLM-backed systems, including training and post-training pipelines, inference serving, model releases, monitoring, retrieval infrastructure, and cost and latency optimization. Partner with AI researchers and engineering teams to move prototypes into reliable production capabilities across customer tenants. Develop ETL and GraphQL features, enforce tenant isolation, and support deployments in regulated or customer-controlled environments.

Manage small technical teams to design and deliver data and cloud solutions (Azure/AWS). Diagnose complex problems, lead SDLC and Agile delivery, architect and implement IT, data-driven, and ML/AI solutions, manage client relationships, mentor staff, handle estimation, planning, risk/change management, and support company growth and culture.

Lead client-facing finance transformation engagements to optimize finance functions through operating model design, process improvement, automation and AI. Manage project teams and client relationships, deliver high-quality recommendations and tools, identify value-creation opportunities across G&A, support business development, and coach junior staff.

Designs, deploys, and scales production AI agents and agentic workflows within enterprise Java and Spring applications. Establishes evaluation, monitoring, guardrail, safety, testing, observability, deployment, and operational standards for LLM systems. Leads architectural decisions across AI services and microservices, productionizes models, evaluates emerging technologies, mentors engineers, and influences technical strategy across teams.

Build and deploy production AI agents, agentic workflows, and ML-enabled enterprise features within Java/Spring microservices. Implement evaluation, monitoring, guardrails, safety, testing, and observability for LLM applications. Collaborate with AI scientists, engineers, product managers, and technical specialists to productionize models, integrate RAG and tool calling, prototype solutions, and improve AI capabilities. The role also involves design reviews, agile planning, software quality ownership, and staying current with emerging AI technologies.

Own end-to-end analytics delivery for a client POD, translating business priorities into workplans, coordinating cross-functional teams, governing delivery and revenue KPIs, and managing risks through resolution. Perform hands-on data profiling, validation, reconciliation, exploratory analysis, automation, and quality reviews using SQL, Python, Databricks, and Excel. Create executive-ready KPI, revenue, and business-impact narratives, prioritize initiatives, support business cases, and identify opportunities for analytics automation and AI-enabled improvements.

Lead the architecture and development of Root’s machine learning platform for insurance pricing. Build infrastructure for feature pipelines and stores, reproducible training and orchestration, model registries, serving, validation, observability, and research-to-production workflows. Establish versioned contracts and technical standards, automate workflows using LLMs, write and review critical code, influence cross-team strategy, and mentor senior engineers. Collaborate closely with data scientists and researchers to create scalable, reliable, and reproducible ML capabilities.

Build production machine learning benchmarks and evaluation systems for foundation models. Own backend infrastructure including data pipelines, execution environments, storage, and orchestration, as well as sandboxed environments for agentic evaluations. Partner with researchers, enterprise customers, and company leadership to scope and deliver technical solutions. Identify scalable evaluation patterns, communicate clearly, and operate effectively in an ambiguous, high-ownership startup environment.

Build and operate scalable ML infrastructure and platform capabilities across cloud and on-premises environments. Develop Python-based developer tooling, services, and production systems supporting experimentation, distributed training, model deployment, and operations. Lead complex initiatives from architecture through rollout, improve reliability and developer productivity, and partner with ML and infrastructure teams. The role emphasizes Kubernetes, AWS, distributed compute, GPU-intensive workloads, observability, and production excellence.

Leads the design, development, testing, and deployment of enterprise automation, RPA, AI/ML models, intelligent agents, APIs, integrations, and data pipelines. The role supports cloud and infrastructure operations through improved reliability, financial accountability, asset optimization, productivity, and data-driven decision-making. Responsibilities include establishing engineering standards, testing, security, governance, CI/CD practices, technical documentation, troubleshooting, Agile delivery, mentoring junior engineers, and collaborating with stakeholders.

You will tackle AI challenges, develop scalable software and collaborate with teams to enhance healthcare navigation using AI solutions.

Partner with functional leaders to translate business strategy into people strategy. Own org design, performance management, compensation, talent development, engagement, and retention. Provide coaching on career pathing, change management, and sensitive employee issues. Use people data to generate insights and recommendations, build compliant HR policies, manage global employee relations, and collaborate with People Team COEs to deliver cohesive people programs at scale.

Serve as second-in-command for Coinbase Derivatives' Market Regulation Department, leading day-to-day regulatory compliance, managing exams and audits, advising on CFTC Core Principles, reviewing surveillance escalations and enforcement actions, and coordinating cross-functional regulatory workstreams to ensure the DCM meets obligations and resolves findings timely.

Lead reservoir characterization, spacing and frac design assessments to develop type curves, forecasts, economics and development plans. Perform A&D modeling, well testing design, lookbacks, and team technical strategy. Use data analytics, Python, and machine learning principles to manage large datasets and improve workflows; mentor junior engineers and share best practices across the organization.

Develop and maintain open-source Python frameworks and ML developer tools, architect scalable libraries, grow early-stage projects, build real-time streaming capabilities, integrate modern frontend technologies, and collaborate with open-source contributors and the broader developer community.

Executive leader responsible for Fluence’s global Software and Controls Engineering organization. Leads embedded software, controls, BMS, EMS, SCADA, cloud, fleet management, cybersecurity implementation, DevSecOps, software quality, and digital product engineering. Establishes engineering strategy, standards, governance, investment priorities, and organizational capabilities while overseeing the full product lifecycle. Partners cross-functionally to deliver secure, scalable, reliable products and drives innovation in AI, machine learning, cloud-native architectures, edge computing, automation, and digital twins.

Drives operational excellence through data analysis, dashboards, process improvements, and cross-functional project leadership. The role embeds with business units to manage day-to-day operations, investigate trends, answer strategic questions, implement workflows, coordinate stakeholders, track deliverables, and present insights to technical and non-technical audiences. It serves as a strategic partner to operations leadership in a fast-paced, ambiguous environment.