Top Machine Learning Jobs in Miami, FL

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.

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.

Develop and implement ML features focusing on NLP and generative AI, collaborate with stakeholders, and enhance our platform capabilities.

Design and build infrastructure for ML model training and deployment. Collaborate with teams and monitor performance using AWS tools. Focus on reliability and efficiency in high-availability environments.

As a Software Engineer, you'll enhance the user experience of ML tools, optimize workflows, and collaborate with product managers on ML infrastructure projects.

Lead the development of AI systems for Unity's engine, focusing on multi-agent systems, reinforcement learning, and intelligent decision-making.

Design and implement foundational machine learning systems, develop conversational interfaces, and improve existing generative AI solutions for hardware engineering.

The Machine Learning Engineer will enhance search retrieval, train ranking models, build query understanding systems, and ensure robust pipelines for Reddit's AI-driven products.

The role involves developing machine learning models for Netflix's Studio, collaborating with teams, and scaling ML solutions for data analytics.

As a Principal Machine Learning Engineer, you will design and develop machine learning applications, lead teams, and ensure project management excellence while engaging with clients.

The engineer will design and maintain auto-bidding algorithms, optimize ad delivery systems, and collaborate on engineering projects for campaign performance.

The Engineering Manager - Data & ML will redesign and scale Underdog's data and ML platform, establishing best practices, leading a team, and collaborating with cross-functional teams to optimize data systems.

Design, implement, and maintain ML solutions using AWS SageMaker. Develop end-to-end ML pipelines and collaborate on model deployment to enhance data-driven decision-making.

Develop and optimize large-scale ML models and training pipelines for automating PCB layouts, collaborating on data pipeline design and novel architectures.

As a Senior ML Ops Engineer, you'll build ML infrastructure, implement automated deployment, optimize model serving, and ensure production performance.

As a Machine Learning Engineer, you will fine-tune transformer models, deploy machine learning solutions, and work with cross-functional teams on innovative projects.

Develop ML models and pipelines for autonomous tractor initiative, improving system safety and performance in production environments.

Leading the AI/ML team, mentoring data scientists and engineers, developing production-grade systems for cybersecurity, collaborating on architecture, and driving best practices for AI development.

The Senior AI/ML Engineer will drive AI/ML strategy, evaluate solutions, collaborate with teams, and innovate in data science.

The Senior Product Manager will lead the strategy and execution of AI-driven features in a SaaS platform, ensuring high-quality user experiences and data-driven decisions while mentoring junior team members.

As an ML Engineer, you will build and deploy machine learning models for autonomous robots, collaborating with ML scientists and engineers to enhance AI capabilities.

Lead ML projects to optimize search and recommendations at Airbnb, collaborating with cross-functional teams to enhance user experience and business impact.

Design, develop, and deploy machine learning systems, improve ML infrastructure, and partner with stakeholders to enhance data science workflows.