Top Machine Learning Jobs in Minneapolis, MN

As a Staff Machine Learning Research Scientist, you will lead ML research, develop evaluation frameworks, mentor researchers, and collaborate on clinical AI solutions.

The role involves designing and building AI components for a platform, specifically focusing on NLP and ML for catalog mapping and anomaly detection, along with feedback mechanisms.

As a Fraud ML Engineer, you will ensure authentic human data is processed by building detection systems using adversarial ML techniques and managing backend systems.

The role involves developing and deploying machine learning solutions, optimizing models, and collaborating with cross-functional teams. Responsibilities include cloud architecture, algorithm engineering, and infrastructure best practices.

The Applied Scientist AI/ML Intern will work on building data pipelines, finetuning LLMs, and deploying AI models in a collaborative team environment.

The Senior Analyst in Predictive Analytics and Machine Learning develops and maintains statistical models to support business decisions, monitors model performance, and collaborates with stakeholders across the company.

Lead a team of machine learning data scientists, overseeing model development for fraud detection and risk assessment, and ensuring quality throughout the model lifecycle while mentoring the team.

As an AI/ML Presales Solutions Architect, you will engage with clients to identify problems, create feasible AI solutions, and partner with sales to ensure project delivery aligns with capabilities. You will leverage your extensive experience in AI and cloud platforms to shape presentations and technical discussions with clients.

Design, build, and scale ML and Generative AI systems. Develop applications using ML architectures and monitor model performance.

The Staff Machine Learning Engineer will lead ML initiatives, develop AI systems, optimize pipelines, and mentor team members to automate healthcare processes.

As an MLOps/ML Platform Engineer, you will build and manage ML systems, optimize workloads, and ensure production model reliability while collaborating across teams.

The Machine Learning Engineer will execute the full ML lifecycle, collaborating on model implementation and improving customer experiences through data-driven insights.

Lead the design and operation of Twilio's ML and data platform, architecting cloud-native pipelines and implementing MLOps best practices while mentoring engineers and collaborating across teams.

As an AI/ML Architect, you will define scalable AI systems architecture, mentor teams, guide clients, and ensure ethical AI practices.

Lead research on humanistic AI for autonomous systems. Collaborate with teams, design experiments, and publish findings in top-tier publications.

As a Machine Learning Engineer, you'll develop and deploy ML models for medical imaging, collaborate with teams, and optimize for production environments.

The Senior Machine Learning Engineer will manage the full ML lifecycle, collaborating with teams to develop scalable systems that improve product decisions and empower monetization strategies.

Lead the end-to-end machine learning lifecycle for AI products, managing data pipelines and model development to optimize performance and accuracy in production.

The Principal Machine Learning Engineer will develop multimodal technologies, mentor engineers, and drive product innovation in agentic AI systems.

Lead the strategy and implementation of AI solutions in R&D, driving adoption and improving operational efficiency through advanced analytics. Collaborate with cross-functional teams to design AI/ML models and evaluate new technologies.

As a Lead Machine Learning Engineer at Root, you'll build scalable ML systems, partner with data scientists, and improve operational practices for customer lifetime value modeling.

Lead the engineering organization for data and AI platforms, making architectural decisions and driving product vision while managing teams and aligning with business priorities.

The Senior AI Engineer will develop AI solutions for incident management, collaborate cross-functionally, and enhance observability tools through iteration and ownership.

The role involves applying established ML techniques to cybersecurity data problems, integrating ML outputs into data pipelines, and ensuring model reliability. Collaboration with engineers and product teams is key, along with strong communication skills and understanding of data flow.

The role involves leading ML client engagements, shaping strategies, advising on ML architecture, and influencing project outcomes, while guiding teams and driving business value.