Top Remote Machine Learning Jobs in Las Vegas, NV

Work with the product team on automating data ETL pipelines, deploying ML models, and connecting ML models to user interfaces. Collaborate with cross-functional teams on product development.

Lead a team of data scientists developing ML-driven products, managing infrastructure, training models, and ensuring production quality while collaborating with senior leadership.

Design, develop, and deploy machine learning models; analyze large datasets; build scalable data pipelines and distributed computing environments; integrate ML solutions with cross-functional teams; optimize performance and support production ML infrastructure.

Lead and grow a global data engineering and ML organization, design cloud-native data platforms and pipelines, deliver ML-powered products, establish engineering and MLOps best practices, and support sales through solutioning and client-facing technical leadership.

As a Machine Learning Engineer, you will develop and operationalize models for content understanding and discovery, collaborating with cross-functional teams and stakeholders to solve impactful problems using machine learning.

Lead the Client Solutions team, ensuring the execution of complex transactions while driving innovation and client advocacy. Oversee operations, build client relationships, and implement strategic frameworks for growth.

Participants are needed for an AI technology research study, offering compensation up to $600 for ~1.5-2 hours of participation over one week, requiring skills in tech-related roles.

As a Senior Machine Learning Engineer, you'll develop models for risk identification, collaborate cross-functionally, and deploy ML solutions, ensuring performance monitoring and continuous improvement.

Lead the vision, strategy, and execution of the machine learning platform at Flex, focusing on compliance, customer success, and internal operations. Build and mentor a team of ML engineers and data scientists to deliver scalable ML systems.

The Business Analyst 2 analyzes AI-powered data solutions, optimizes customer experience automation, and collaborates with teams to drive business insights and value.

The Research Scientist will conduct studies on natural language processing and machine learning, transition models to production, conduct experiments, publish research, and create new models to enhance products.

The Engagement Principal is responsible for the deployment of Aera solutions, managing client relationships, project delivery, and contributing to sales cycles, ensuring successful implementation and value delivery.

The Marketing Manager will lead strategic campaigns, manage content, collaborate with teams, analyze performance, and stay ahead of industry trends to drive brand awareness and customer engagement.

Lead a team to develop groundbreaking technology combining NLP, big data, and AI to enhance digital communication and interaction.

The GTM role focuses on increasing revenue through new sales and partnerships, understanding client needs, and communicating product value.

As an ML Solutions Architect, design, implement, and lead machine learning solutions for Conversation AI, collaborating with cross-functional teams to address complex business challenges.

The role involves designing and deploying a data lakehouse infrastructure, building ML pipelines, monitoring services, and collaborating with teams to optimize AI systems.

The Machine Learning Engineer will develop software solutions, optimize data pipelines, and deploy machine learning models using cloud platforms and best practices.

The Forward Deployed ML Engineer will integrate AI/ML models into clinical software, assist in model fine-tuning, and engage with clients to ensure success in healthcare deployments.

The position involves driving AI/ML product development for personalized experiences, partnering across teams, and launching impactful products.

Join a team focusing on end-to-end ML Ops for the insurance sector. Develop and deploy ML solutions, build infrastructure, and solve business challenges using advanced technologies.

As a Machine Learning Engineer at Shipwell, you'll build and scale AI-powered logistics solutions, maintaining data pipelines and ML infrastructure across the organization.

Design, operate, and optimize GPU-backed ML/LLM inference infrastructure in cloud environments. Build scalable GPU clusters, implement high-throughput and distributed inference, integrate optimization stacks, schedule heterogeneous workloads, and add observability to improve performance and reliability.

Work on Wayfinding projects using LLMs and agentic workflows, collaborate with engineering/product/research teams, receive mentorship, and gain hands-on experience with model evaluation, prompt engineering, and shipping code in a real engineering environment.

The Director of Product Engineering leads the product engineering organization, overseeing technical direction, talent management, engineering excellence, and collaboration across teams to deliver secure, reliable digital products.