Top Machine Learning Jobs in Minneapolis, MN

Design and implement backend ML systems for personalized feeds at Reddit. Mentor teams, drive technical discussions, and improve MLOps tools.

Develop and support ML infrastructure for large-scale scientific workflows, improve automation processes, and contribute to product features in AI-enabled drug discovery.

Lead the design and development of machine learning infrastructure for robotics, defining strategies, collaborating with teams, and ensuring best practices in MLOps.

The role focuses on building scalable backend systems for ML features, collaborating on model adaptation, ensuring production reliability, and API design.

Design and develop scalable machine learning systems for fraud insights, collaborating with teams to deliver high-quality ML infrastructure effectively.

As a Principal ML Engineer, you will design ML architecture, tackle technical challenges, mentor engineers, and influence ML strategy across the company.

The Senior Data Scientist will develop and deploy machine learning and AI models to enhance business strategies in a B2B SaaS context, collaborating with cross-functional teams to transform data into actionable insights.

Design and implement software solutions for machine learning operations, focusing on geospatial imagery analytics and MLOps software development.

Develop AI-driven features and systems, focusing on LLM fine-tuning and integration with third-party tools. Stay current with AI advancements.

The ML Scientist will lead data science and ML projects, automate reporting, support product development, and maintain ML infrastructure at Harvard Business Publishing.

Lead the design and implementation of deep learning models for biological data, focusing on model architecture, project leadership, and innovation in machine learning applications in biology.

As a Staff Machine Learning Engineer, you will develop, productionize, and deploy ML models for clinical use, ensuring real-world effectiveness through hands-on coding and infrastructure building.

The Staff Machine Learning Engineer will develop predictive models for financial metrics and work closely with data teams, ensuring collaboration and integration of high-quality models.

Lead the development of identity resolution systems using machine learning techniques, overseeing end-to-end workflows and collaborating with cross-functional teams.

The role involves designing and maintaining ML infrastructure, ensuring performance and security, collaborating with ML engineers, and implementing modern AI capabilities.

The Sr. Staff Machine Learning Engineer will lead architecture and development of ML systems, enhance patient interactions, optimize treatment recommendations, and mentor engineers.

Develop and enhance AI/ML capabilities for Yahoo Mail, focusing on user personalization, collaboration with teams, and data-driven insights using machine learning techniques.

Develop and enhance data storage solutions for analysis by experts and machine learning. Collaborate in an Agile environment, create proofs of concept, and ensure security best practices. Engage with application engineers to implement scalable solutions and conduct testing.

Lead the development of data science solutions for inventory and logistics planning, optimize planning processes, and deploy predictive models. Collaborate across functions and present insights to stakeholders.

Lead the Safety ML team at Discord, developing AI/ML infrastructure, collaborating on safety challenges, and mentoring engineers while driving technical strategy and innovation.

As a Senior Data Scientist, you will collaborate across teams to enhance data science, analyze models, and improve product features to aid student writing.

The ML Engineer will build, deploy, and iterate on ML systems, focusing on classification, human feedback loops, and working with real-world data.

Transform language models into real-world applications by fine-tuning, engineering data, evaluating models, deploying AI features, and collaborating with teams.

The role involves fine-tuning diffusion models, curating datasets, developing evaluation frameworks, and optimizing their performance for AI applications.

Develop and optimize multimodal neural simulators for robotic tasks, managing end-to-end AI engineering including model training and deployment.