Top Remote Machine Learning Jobs in Minneapolis, MN

The role involves managing the ML lifecycle, developing scalable ML systems, and collaborating on AI-first product solutions focusing on Large Quantitative Models.

The Solutions Engineer will provide technical support for customers, ensure satisfaction, communicate with sales, and analyze customer needs for product enhancements.

As the Engineering Lead, you will guide product strategy, manage and mentor engineers, and ensure successful execution in product areas, focusing on machine learning and technical expertise across teams.

The Product Manager will lead product development, working with cross-functional teams, driving growth, and creating user experience frameworks.

Lead design, build, and production deployment of ML and GenAI solutions (LLMs, RAG, multi-agent systems, recommender and RL models). Architect scalable, secure inference and MLOps pipelines, mentor MLEs, collaborate with stakeholders to define roadmaps, and measure model performance and business impact. Implement fine-tuning, retrieval, orchestration, and monitoring for enterprise-grade AI products.

The Senior Manager, Data Science/ML will lead a team to enhance product innovation through data science, focusing on ML and experimentation frameworks that boost customer engagement. Responsibilities include defining product strategy, building ML models, and collaborating with cross-functional teams to shape product roadmaps.

Paid 3-month remote contract for students or recent grads to build and test LLM-based solutions using the Token Factory inference platform. Assist senior SAs with prompt engineering, model selection, benchmarking, and inference optimization. Run performance and quality experiments and contribute to internal tooling and automation. Mentorship provided; strong performers may be considered for full-time roles.

Lead development and maintenance of internal Python SDK frameworks and agentic workflows, build scalable FastAPI-based ML APIs, automate CI/CD with Jenkins and GitLab Runners, use Galileo for model evaluation and observability, and collaborate across teams to deliver production-grade autonomous systems.

The AI & Machine Learning Director will lead client engagements, analyze corporate performance, and drive improvements in profitability through machine learning and data analysis, while also managing business development and team collaboration.

The Senior Vice President will lead projects focused on corporate performance, drive revenue growth, and develop business strategies while utilizing data modeling and machine learning techniques for analysis and solutions.

The Senior Machine Learning Engineer will deploy and maintain ML models, collaborate with cross-functional teams, and optimize ML system performance in a fully remote role.

Lead the design, development, and deployment of scalable machine learning and AI systems for enterprise clients, guiding architecture and mentoring engineers.

As VP of Engineering, you'll lead the application engineering strategy and team, integrating AI capabilities into products while ensuring quality and execution across backend, mobile, and desktop platforms.

Lead the development of production-grade machine learning systems, build ML pipelines, and optimize models for deployment and performance.

Design and deploy production recommendation and retrieval engines, build data orchestration pipelines for multiple modalities, implement agentic workflow frameworks, and prototype, optimize, and scale models with monitoring, governance, and low-latency SLAs.

The role involves product management of AI tools, collaborating with research, engineering, and design teams to develop and enhance products, focusing on user needs and market value.

The Staff Machine Learning Engineer will enhance Quora's recommendation systems, lead machine learning projects, and own end-to-end ML processes.

The role involves improving recommendation systems using machine learning, developing end-to-end ML systems, and collaborating with engineers.

The DevOps Engineer will design, build, and optimize ML and data infrastructure on GCP, mentor a team, and ensure system reliability and performance.

The Lead Machine Learning Engineer will optimize AWS SageMaker, manage ML pipelines, develop infrastructure as code with Terraform, build CI/CD pipelines, and collaborate with data scientists to deploy models in production.

The role involves leading ML strategy, architecting systems, designing algorithms, collaborating with teams, and mentoring engineers in AI for enterprise analytics.

The AI/ML Engineer develops AI features for Cengage's educational products, focusing on enhancing student engagement and learning outcomes through shipping production AI capabilities.

Lead design, development, and production deployment of ML models and GenAI/LLM solutions to optimize clinical trial workflows. Build scalable pipelines, monitoring, and CI/CD for robust production use. Collaborate cross-functionally, mentor junior engineers, and communicate technical results to technical and non-technical stakeholders.

The Customer Success Manager will drive user adoption and value realization for ISP customers, manage onboarding and retention, and collaborate cross-functionally to enhance customer satisfaction and business growth.

Lead and mentor engineering teams while owning platform architecture (React, Node/Express, Postgres, AWS). Drive TypeScript migration, design ETL and AI/LLM integration frameworks, ensure reliability and observability, manage delivery, incidents, and hiring, and define technical strategy for production ML/AI systems.