Top Senior Machine Learning Jobs in Houston, TX

As a Staff AI Researcher, you will develop AI solutions, work with complex data sets, redesign systems, and implement AI/ML techniques for health improvement.

The Senior Machine Learning Researcher will design experiments, research new algorithms, and collaborate on deploying models for computer vision and robotics applications.

Design, build, and own production ML and LLM-driven features (RAG, prompt engineering, fine-tuning, semantic search). Write maintainable code, create design docs, troubleshoot issues, collaborate with product/design, and participate in Agile development. Work with Python/Java/JavaScript, ML frameworks, and cloud/container technologies.

Lead Lean transformation initiatives and AI coordination to enhance process excellence across multiple business functions, coaching teams and ensuring integration with operational standards.

Design, build, and operate high-performance ML inference platforms for LLMs, vision, and recommendation models. Focus on batching, autoscaling, GPU utilization, routing, observability, security, deployment workflows, and incident response to ensure low-latency, cost-effective, multi-tenant model serving in production.

Develop and optimize ML/DL compiler technologies and runtimes for NVIDIA hardware to accelerate LLMs and other AI workloads. Design kernel and DSL solutions, build efficient attention runtimes, automate development flows with AI, and co-design systems with internal hardware and software teams.

This role leads Revenue Growth Management initiatives, linking data analytics with executive strategy to drive business growth through actionable insights and project management.

As a Staff Machine Learning Research Scientist, you will lead ML research, develop evaluation frameworks, mentor researchers, and collaborate on clinical AI solutions.

Lead a team of machine learning data scientists, overseeing model development for fraud detection and risk assessment, and ensuring quality throughout the model lifecycle while mentoring the team.

The Head of Product will drive strategic initiatives, lead product development, partner with executives, and ensure alignment with business objectives in the fintech industry.

The Director of Statistics leads statistical initiatives in phase 3b/4 and RWE studies, provides expertise in HTA and regulatory compliance, and collaborates with cross-functional teams. Responsibilities include statistical analysis plans, Bayesian methodologies, and mentoring junior staff.

The Senior AI Engineer at RegScale designs and operates AI systems, ensuring reliability and performance while overseeing the data pipeline and compliance integration, alongside improving AI capabilities across the engineering organization.

Design, build, and deploy end-to-end data science solutions for pricing, underwriting, claims, and operations. Perform analyses on structured and unstructured data, develop and monitor ML models, create dashboards and visualizations, implement data pipelines, and collaborate cross-functionally to prioritize analytics initiatives and communicate results to technical and non-technical stakeholders.

As an AI Applications Engineer, you will bridge AI research and implementation, driving experiments, building infrastructure, and releasing products to improve user experience and metrics.

As Lead Data Scientist, you will develop machine learning and generative AI solutions for financial planning and strategic decision-making, mentor team members, and engage with finance leadership.

The role involves defining the vision and strategy of the Outreach AI Platform, prioritizing the roadmap, collaborating with engineering and data science teams, and engaging with customers to refine AI strategies.

Lead Data Science initiatives, build AI/ML solutions, and mentor teams in a collaborative environment, driving innovation in a leading construction equipment venture.

Lead design, build, and operation of end-to-end production ML systems for ranking, rewards, ROAS/LTV prediction, personalization, and offer optimization. Own feature pipelines, training, inference, experimentation, monitoring, retraining, and MLOps. Partner with Data Engineering, BI, Product, and Engineering to connect ML to business outcomes, mentor engineers, and drive an AI-first engineering culture.

Design, build, deploy, and operate production-grade AI/ML and LLM systems (including RAG and agentic pipelines). Integrate AI capabilities with product workflows, run evaluation and monitoring, optimize model and system performance, and apply strong engineering practices (testing, CI/CD, versioning).

Design and implement production ML features, close behavior gaps with common frameworks, investigate divergences, improve performance and stability, and document designs and tests for geospatial and large-scale ML workflows.

Lead design, development, and delivery of ML, DL, and Generative AI systems for production. Apply prescriptive analytics, optimization, recommendation, and reinforcement learning. Build and evaluate models, run experiments, develop prototypes, benchmark methods, and collaborate with business partners to translate data into actionable recommendations and influence strategy.

Design, build, and operate end-to-end ML pipelines including data ingestion, feature engineering, training, deployment, and monitoring. Deploy and scale models on AWS or GCP, implement CI/CD, containerization, orchestration, model lifecycle management, observability, and mentor junior engineers to productionize personalization, recommendation, and NLP solutions.

Design, develop, and deploy end-to-end machine learning models using statistical and ML algorithms. Prepare and enrich data, implement scalable PySpark/Databricks solutions, and collaborate with product and engineering to create personalized customer experiences and actionable insights.

Design, develop, fine-tune, and deploy generative AI models (GANs, VAEs, transformers). Collaborate with cross-functional teams, troubleshoot model issues, document work, and communicate technical concepts to non-technical stakeholders while staying current on research.

Design, develop, fine-tune, and deploy generative AI models using deep learning and transformer architectures. Collaborate cross-functionally, troubleshoot model issues, optimize performance, document work, and communicate technical concepts to non-technical stakeholders.