Top Machine Learning Jobs in Houston, TX

Design, build, and deploy production ML systems for ICS/xOT cybersecurity, including threat detection, anomaly detection, NLP/LLM features, data pipelines, observability, MLOps, and containerized deployments across cloud and on-prem environments.

Owner of customer success for a portfolio of industrial accounts, leading project delivery from scoping to commissioning and sustainment. Drive value realization, risk mitigation, cross-functional coordination, executive communication, and identify expansion opportunities while liaising between customers and product/R&D.

Design and govern enterprise AI architectures and roadmaps, lead solution design using LLMs, RAG, vector DBs and AI agents, establish LLMOps and evaluation frameworks, ensure security/compliance and scalability, and provide technical leadership and mentorship across AI initiatives.

Design, build, and support high-throughput, low-latency LLM inference and data pipelines; collaborate with data scientists to post-train and evaluate models; implement scalable, production-ready systems with strong testing, monitoring, and performance focus; mentor engineers and iterate on architecture, reliability, and user-facing AI applications.

Lead research and engineering of production-grade LLM/GenAI systems for cybersecurity: design models and architectures, drive data labeling and evaluation, collaborate with product and engineering to deploy scalable solutions, mentor data scientists, set technical strategy, and advance AI safety, red-teaming, and applied ML innovations for detection and operational use.

Lead development and deployment of large-scale LLM and GenAI solutions for cybersecurity. Drive research strategy, model training, evaluation, safety/red-teaming, and cross-functional productization. Mentor data scientists, set technical standards, and represent the team through publications and thought leadership.

As a Staff Machine Learning Engineer, you will design AI products on Edge devices, optimize ML model performance, and work with large-scale datasets, collaborating across teams to deliver impactful solutions.

As an ML Ops Infrastructure Engineer, you will design CI/CD pipelines for ML, maintain deployment systems, and implement monitoring while collaborating with research teams to ensure model quality and performance.

As a Systems Architect, you'll define and drive the infrastructure architecture for AI/ML workloads, design multi-cloud strategies, manage compute orchestration and storage systems, and lead capacity planning, while also collaborating with engineering leadership.

The engineer will build and operate AI/ML infrastructure, managing services on AWS and bare metal, using tools like Kubernetes and Terraform.

This role entails using statistics and machine learning to analyze complex datasets, implementing algorithms, and back-testing models. A PhD is required for this position.

The intern will utilize machine learning and statistics to extract patterns from data, implement algorithms, back-test models, and communicate findings effectively.

Lead enterprise infrastructure, cloud, data center, networks, endpoints and service desk teams. Own ITSM-driven incident/request/problem management, asset lifecycle, vendor and budget management, change governance, performance metrics and service improvements. Drive secure, resilient operations across corporate and offshore assets, coach managers, and advance DevOps, automation and IT service integration.

The Sr. Machine Learning Engineer will develop and deploy ML solutions for healthcare, manage data pipelines, and work with large datasets to enhance healthcare delivery.

As a Staff Applied ML Scientist, you will develop and maintain fraud detection models, conduct research on fraud types, and collaborate across teams to drive product development and real-time decision-making.

As a Senior Applied ML Scientist, you'll develop fraud detection models, research new fraud types, and write production code while collaborating across teams to enhance SentiLink's identity verification solutions.

Design, train, and productionize machine learning models for fraud and identity risk across the full ML lifecycle. Research new fraud types, perform feature engineering and labeling, write production-ready code for real-time decisioning, monitor models, and provide analyses to inform product, sales, and data acquisition.

Lead client engagements for telco and AI initiatives: manage project delivery, coach teams, drive commercial relationships, identify business development opportunities, and apply quantitative analysis (SQL/Python) and AI capabilities to deliver strategic recommendations.

Lead and manage AI-focused consulting engagements: define hypotheses, oversee analytics and data collection, translate client needs into technical requirements, support ML/generative AI solution delivery from proof-of-concept to production, build executive relationships, and coach consultants to deliver measurable business impact.

Design and deliver secure, high-performance microservices and APIs. Develop backend services in Java and Python, integrate ML components, build front-end service layers, deploy containerized applications to cloud platforms, and collaborate in Agile teams to ensure scalable, test-driven solutions.

The Cloud Inference Engineer is responsible for building and maintaining a scalable LLM inference platform, focusing on distributed systems and machine learning technologies while collaborating across teams to optimize performance.

Lead product strategy and execution for a B2B SaaS marketing analytics platform. Translate research into requirements, own the backlog, partner with engineering and GTM, define success metrics, and ship API- and data-heavy features for technical users.

The Senior Product Manager will set product direction, own the development lifecycle, collaborate with teams, and ensure high-quality outputs. This role requires strong opinions on product quality and customer insights.

Early-to-mid career Product Manager to translate customer needs into product requirements for AI-driven scientific SaaS. Work cross-functionally with engineering, AI/ML, solutions, and commercial teams to prioritize backlog, run discovery, support launches, gather customer feedback, and define product metrics for customers in chemistry, manufacturing, and energy.

Lead the Decision Science function, designing ML models, overseeing experimentation frameworks, and ensuring model adoption across the company, driving operational and commercial improvements.