Top Machine Learning Jobs in Minneapolis, MN

The Senior Product Architect will design security products, collaborating with product management and engineering teams, focusing on architecture and functionality for high-level product features.

Lead the Data Sources product roadmap for document parsing, fraud detection, and bank aggregation. Drive metrics (parse accuracy, fraud catch rate, connection success, SLAs), write specs, engage clients, mentor PMs/engineers, and represent the platform externally. Prioritize document types, extraction models, reliability, and scalability to improve conversion, client satisfaction, and operational efficiency across mortgage, tenant screening, and government use cases.

As a Data Scientist, you will build predictive models, develop software tools, and analyze geoscientific data to support mineral exploration and discovery.

The Data Scientist will develop predictive models, analyze geophysical data, and collaborate with geoscientists to enhance exploration programs, focusing on mineral deposits.

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.

The Machine Learning Engineer will develop editable visuals combining computer vision and machine learning, enabling chat-driven figure editing and leveraging biological understanding to produce accurate visuals.

Lead client-facing AI program governance and delivery as PMO manager. Translate business objectives into AI roadmaps and OKRs, oversee AI/ML lifecycle, ensure data readiness, model validation, deployment, and responsible AI compliance. Manage PMO standards, portfolio prioritization, AI-augmented reporting, and lead executive communications while mentoring PMs, TPMs, and analysts.

AVIA seeks experts in Digital Health and AI for advisory roles to support healthcare systems' technological transformations as part-time, project-based consultants.

Lead and scale full-stack engineering teams for Yahoo Mail; define engineering vision and roadmap; drive AI-native development and AI transformation using Generative AI/LLMs and agents; partner with Product, Design, and Data Science to deliver scalable cloud-native mobile and web experiences; hire, mentor, and retain engineering talent while guiding architecture, technical reviews, and platform modernization.

Lead Pearl's Enterprise AI product business end-to-end: define vision and GTM, build and scale AI+human offerings, partner with enterprise customers, and run a lean cross-functional team to deliver measurable commercial outcomes.

Senior individual-contributor role leading 3Cloud's enterprise AI strategy and Center of Excellence. Advise C-suite clients, define AI architectures and standards, drive adoption, develop offerings, and convert strategy into implementation while mentoring technical leaders and shaping commercial growth.

Lead and grow a small ML engineering team while staying hands-on: design and ship GenAI features, build agentic LLM systems (NER, classification, retrieval, summarization), partner with PM and engineering, scope ambiguous problems, and reduce COGS through ML automation.

Lead enterprise AI and transformation engagements—own customer relationships, scope buildable solutions with FDE, drive commercial outcomes and expansion, advise C-suite on AI strategy, and create repeatable, scalable offerings to evolve Tive into an AI-powered intelligence platform.

Build full-stack marketplace features and ML Ops infrastructure for search, ranking, and renter-facing products. Develop model deployment pipelines, feature stores, real-time data pipelines, and monitoring using Chalk and Vertex AI. Collaborate with data scientists to productionize models, improve system performance and reliability, write tested code, participate in reviews, plan technical work, and communicate effectively across distributed teams.

Build full-stack marketplace features for search, ranking, and renter-facing products while supporting ML Ops infrastructure. Develop model deployment pipelines, feature stores, real-time data pipelines, monitoring, and production integrations with data scientists. Write tested code, review contributions, plan technical work, and improve system performance, reliability, and scalability. Collaborate with product and globally distributed engineering teams.

Design, optimize, and deploy deep learning models for customer behavior prediction. Perform feature engineering, implement research-informed architectures, scale training pipelines, collaborate with client teams, document findings, and uphold engineering best practices.

Design, build, and productionize ML and AI systems (LLMs, embeddings, recommendations, forecasting). Develop end-to-end ML workflows, scalable data pipelines, semantic models, and CI/CD for ML. Collaborate with product and engineering, mentor engineers, and influence platform architecture and standards.

Lead development and productionization of large-scale ranking and personalization ML systems. Build data pipelines, features, and models (batch and real-time), collaborate cross-functionally, run experiments, and improve infrastructure for search, recommendation, and marketplace optimization.

Lead development and optimization of the compiler stack for ML systems, collaborate across teams, integrate and deploy products, map ML operations to hardware, and drive compiler infrastructure innovation and performance debugging.

Design, develop, and deploy end-to-end AI/ML applications (RAG, chatbots, agentic workflows). Build scalable data pipelines, apply graph analytics, entity resolution, and NLP for risk scoring, anomaly detection, and investigative prioritization. Develop models, REST APIs, and deploy/manage solutions in AWS to support federal mission use cases.

The role involves developing and deploying machine learning models for forecasting within the Camus platform, requiring collaboration and technical depth in ML.

The Solutions Engineer will drive the adoption of AI-powered solutions in the electric utility sector, providing technical consulting and customer enablement. Responsibilities include gathering requirements, architecting solutions, leading RFI/RFP processes, and delivering training. Extensive travel is required for client engagement.

Design, operate, and scale ML data and training pipelines for autonomous driving. Build distributed training, orchestration, CI/CD and infrastructure-as-code; maintain data/metadata stores and tooling to support large-scale model training and evaluation across cloud and cluster environments.

Design and implement ML infrastructure for scalable deployment, collaborate across teams, optimize ML systems, and enhance tooling.

The Senior Machine Learning Engineer will build and own production ML systems, manage end-to-end workflow, debug issues, and mentor others.