Top Remote Senior Level Machine Learning Jobs in Dallas, TX

Design and implement scalable APIs and integration services for Skylar's AI platform, focusing on backend development and security systems. Collaborate with engineering teams to ensure reliable integration and mentor junior engineers.

As a Senior AI Software Engineer, you will design and maintain AI systems, improve operational efficiency, and mentor engineers while building scalable applications for loan origination and servicing.

As a Staff Machine Learning Engineer - AI Tech Lead, you will lead the design of AI systems for security operations, focusing on scalable architectures, AI safety, and mentoring. Responsibilities include overseeing technical evaluations, production workflows, LLM fine-tuning, and collaboration with teams to enhance AI capabilities.

Design, develop, and maintain features for Gradio and Trackio, architect Python frameworks, collaborate with contributors, and enhance user experience for ML web apps.

Design and maintain datasets, build and evaluate domain-specific ML and vision-language models for real-time privileged access threat detection; deploy and optimize Python/Docker inference services integrated with WebSocket/WebRTC and protocol-level interfaces; monitor and document production models.

The Senior ML Engineer will develop and optimize machine learning solutions for audience intelligence, build production pipelines, and collaborate with cross-functional teams to enhance user engagement and revenue.

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 Staff Machine Learning Engineer will lead ML initiatives, develop AI systems, optimize pipelines, and mentor team members to automate healthcare processes.

As an ML/AI Engineer, you'll build and operate the LLM platform, integrating models, optimizing performance, and collaborating cross-functionally to ensure reliable AI systems.

Design, iterate, and productionize ML models for recommendation, ranking, routing, and monetization. Lead A/B testing and causal measurement, build dashboards and analytics, define metrics, collaborate with Product and Engineering, and scale ML/analytics infrastructure and model lifecycle management.