Top Remote Machine Learning Jobs in Charleston, SC

As a Generative AI Scientist at Mission Cloud, you will leverage generative AI models to build internal products, collaborate with teams, and contribute to groundbreaking AI solutions. Responsibilities include designing and deploying AI models, collaborating with interdisciplinary teams, and staying updated on AI trends. Requirements include experience in AI model implementation, programming skills in Python and AI frameworks, and AWS certifications. This remote position offers a rewarding package and career development opportunities.

Design, develop, and program methods, processes, and systems to analyze big data sources and generate actionable insights. Collaborate with product and service teams to identify questions and issues for data analysis and experiments. Develop software programs, algorithms, and automated processes to cleanse and evaluate large datasets. Communicate insights and findings to stakeholders.

Design, build, and enhance batch and real-time inference services and tooling that support machine learning use cases. Collaborate with ML modelers to encourage adoption of new tools and technologies and plan for future needs of the ML teams.

As a Staff Machine Learning Engineer at Striveworks, you will be responsible for leading machine learning projects, developing ML models and analytics solutions, automating data pipelines, and managing project execution teams. This is a customer-facing role with a remote work environment and some travel required. The anticipated base pay range is $200,000–$250,000/year.

Work as a machine learning engineer in the Applied AI and Machine Learning team. Contribute to core recommender systems and machine learning models. Opportunity to work on a large-scale machine-learning system and continuously learn and improve the solution.

Lead the strategic vision for advancing Machine Learning solutions, champion MLOps excellence, mentor junior engineers, collaborate with product teams, and maintain machine learning infrastructure.

Design, develop, and deploy machine learning solutions to tackle practical challenges in the marketplace. Collaborate closely with product managers, data scientists, and backend engineers to deeply understand business needs and create impactful ML applications. Actively engage with diverse stakeholders to ensure that solutions are well-integrated and aligned with business goals. Push the envelope on operational efficiency by continually refining and advancing algorithms and models.