Top Remote Machine Learning Jobs in Houston, TX

Senior ML research leader who translates state-of-the-art ML pipeline insights into product requirements, reference architectures, benchmarks, and platform roadmaps. Partners cross-functionally and with strategic customers and research institutions to set evaluation standards, reproducibility, and responsible ML practices while providing technical leadership and influencing product and executive audiences.

Design, architect, and deploy generative AI/LLM solutions from prototype to production; work directly with clients to translate business needs into technical plans; write production-quality code integrating LLMs and ML models; collaborate with cloud, DevOps, and frontend teams; support proposals, sales, and evaluation/observability of models.

Lead definition and implementation of the companys ML strategy and infrastructure. Build end-to-end production ML systems (data pipelines, training, evaluation, serving), own data strategy and feedback loops, integrate ML with product/backend, write production-quality code, and recruit/mentor the ML team as it scales.

Serve as the presales technical expert for ML capabilities: lead discovery, architect ML solutions, run demos/workshops/proofs-of-concept, coordinate cross-functional teams, and inform product direction based on customer feedback.

The Solutions Engineer will drive the adoption of AI-powered solutions in the electric utility sector, providing technical consulting and customer enablement. Responsibilities include gathering requirements, architecting solutions, leading RFI/RFP processes, and delivering training. Extensive travel is required for client engagement.

As a Staff Machine Learning Engineer, optimize card pricing models, lead ML lifecycle, collaborate with experts, and enhance predictive features.

As an AI/ML Ops Engineer, you will architect and operate ML pipelines, automate CI/CD processes, and collaborate with cross-functional teams to enhance engineering productivity.

The role involves deploying, optimizing, and maintaining ML-driven motion planning and control algorithms for autonomous driving, collaborating across teams to ensure reliability under performance and safety constraints.

As a ML Engineer focused on inference, you'll design and optimize deep learning models for real-time communication AI applications, collaborating with cross-functional teams and ensuring production readiness.

As a Research Intern, you will work on AI/ML projects, contributing to the development of the Nebula LLM and applying machine learning techniques to real-world problems.

Design and maintain scalable machine learning infrastructure, collaborating with data scientists, ensuring performance, security, and compliance of AI systems.

The ML Engineering Intern will contribute to the Nebula LLM development, tackle machine learning challenges, and gain hands-on experience in AI and language technology.

The AI Director will oversee ML bot development and implementation, identify AI opportunities, educate teams, and prototype AI solutions.