Top Machine Learning Jobs in Minneapolis, MN

Build and scale production AI systems for Prophetic’s real estate intelligence platform. Responsibilities include developing agentic systems, data pipelines, inference workflows, evaluation benchmarks, geospatial processing, and machine learning applications across hundreds of millions of records. The role requires shipping reliable Python services, optimizing throughput and cost, integrating research into products, and collaborating with engineering and product teams.

Design, build, and operate production agentic AI systems for enterprise clients. Develop autonomous agents using Glean, OpenAI/GPT Enterprise, and AWS or GCP. Own agent workflows from architecture and implementation through deployment, monitoring, evaluation, security, and iteration. Apply tool calling, RAG, multi-step orchestration, memory, prompt engineering, structured outputs, permission-aware retrieval, and cost and latency optimization. Measure reliability and quality using evaluations and maintain enterprise-scale production software.

Serve as the primary technical resource for enterprise manufacturing accounts, designing and deploying integrations between Oden and factory systems. Build connector libraries, configure AI and ML solutions, troubleshoot distributed systems and data pipelines, analyze large datasets, and support customer deployments. Partner with customers, Data Science, Product, and Engineering teams to manage technical risks, improve platform capabilities, and document repeatable implementation practices. Travel to customer sites approximately 25–40%.

Design, train, and deploy ML models for Qur'ān recitation, tajwīd detection, and ASR. Build data pipelines for cleaning, segmentation, labeling, and evaluation. Integrate real-time predictions via APIs or lightweight on-device inference, optimize models for speed and mobile performance, and collaborate with backend developers to ship AI features in the app.

Designs integration architecture for third-party agentic AI tools within secure, vendor-isolated government enclaves. Defines data packages, identity federation, tenant isolation, audit logging, and PHI/PII governance. Provides hands-on expertise in LLMs, RAG, fine-tuning, tool use, multi-agent orchestration, and LLMOps. Guides AWS and Databricks-based AI/ML deployments, evaluation, monitoring, and automation while presenting architectures to technical and non-technical stakeholders.

Lead development and deployment of predictive and ML models for credit risk, collections, and operations. Mentor data scientists, promote MLOps and modern tools, present insights to stakeholders, and drive experimentation with techniques such as causal inference and GenAI.

Develop and deploy machine learning models and production software for multi-object tracking in autonomous trucks. Responsibilities include sensor fusion across camera, LiDAR, and radar; scalable PyTorch training workflows; tracking algorithm development; large-scale data analysis; C++ and Python engineering; safety and validation strategy; architecture decisions; and cross-functional collaboration. The role also provides technical leadership through design reviews, code reviews, mentoring, and engineering best practices.

Leads the Underwriting Data Engineering team and oversees the design, development, and productionization of scalable data and machine learning pipelines. Responsibilities include feature engineering, data preparation, model deployment, data validation, governance, monitoring, API integration, analytic data products, roadmap development, stakeholder collaboration, and team coaching.

Lead the vision, strategy, roadmap, and end-to-end lifecycle for AI-driven enterprise applications in the legal technology domain. Guide development of production-ready AI, LLM, and agent solutions; optimize transactional workflows; and collaborate with engineering, data science, design, marketing, sales, and customer success. Ensure products meet high standards for security, usability, reliability, and quality while communicating progress and aligning stakeholders.

Own end-to-end analytical lifecycle to develop and optimize business, risk, and growth strategies. Monitor portfolio and macro trends, build and interpret ML-driven models, lead cross-functional launches, mentor junior analysts, and build AI-assisted workflows to improve decisioning and measurement across products and geographies.

Lead AI/ML strategy for Medicare & Retirement, identify and prioritize high-impact AI use cases, secure funding, and oversee end-to-end delivery of production-grade AI/ML and Agentic AI solutions. Build and mentor large AI engineering teams, embed responsible and secure AI practices, partner cross-functionally, and communicate outcomes to senior leadership.

Develop agentic AI systems and machine learning methods for drug discovery, including molecular and tabular in-context learning, few-shot adaptation, foundation models, and biological data analysis. Lead research projects from task definition and dataset construction through model training, benchmarking, ablations, and scientific validation. Collaborate with chemists and biologists, build reproducible research software, scale training pipelines, and translate successful methods into drug discovery capabilities and scientific publications.

Leads the design, development, and deployment of AI-powered healthcare solutions using Java, Spring Boot microservices, HL7 FHIR, Da Vinci guides, Kafka, relational databases, Docker, Kubernetes, and cloud platforms. Builds REST APIs and event-driven systems across claims, eligibility, prior authorization, and provider domains. Establishes CI/CD automation, observability, security, and responsible AI practices while evaluating emerging technologies and improving operational workflows.

Lead and scale Anaplan’s AMER Partner Success organization. The role owns partner enablement, delivery excellence, readiness measurement, and joint operating models across consulting, technology, systems-integrator, ISV, and cloud partners. Responsibilities include managing and coaching Partner Success specialists, coordinating with sales, marketing, product, professional services, and customer success teams, improving partner-led implementations, tracking KPIs, advocating for partners, and driving growth, adoption, revenue, retention, and customer outcomes through the partner ecosystem.

As a Customer Success Engineer, you'll drive adoption and satisfaction for enterprise customers using Deepgram's voice AI technology, fostering strong relationships and aligning technical solutions with business objectives.

Own product areas for an autonomous banking platform, translating backend capabilities (APIs, async workflows, ML) into user-facing features. Conduct customer discovery, define technical requirements and acceptance criteria, prioritize backlog, partner with engineering on scoping and de-risking, and validate outcomes using feedback loops and analytics.

Lead the Product and Design organization, define product vision and roadmaps, prioritize strategic investments, and align cross-functional teams to deliver safe, scalable robotic delivery experiences. Build and develop high-performing product and design teams, champion user-centered design, guide data-driven decisions, and translate complex robotics, hardware, software, and operational capabilities into customer value. Partner with executive leadership across Engineering, Operations, Safety, Policy, and Business Development to drive commercialization and growth.

Lead performance marketing data science initiatives focused on lead segmentation, bidding optimization, targeting, and marketing spend allocation. Develop and deploy production-ready machine learning systems, establish research and ML operations roadmaps, build monitoring frameworks, and identify high-impact opportunities. Partner with engineering, marketing, vendors, and executives while mentoring data scientists and machine learning engineers and promoting rigorous, maintainable, and reproducible practices.

Build and deploy AI agents, intelligent applications, scalable backend services, APIs, and full-stack solutions. Integrate large language models, AI services, data platforms, and external APIs into customer-focused products. Collaborate with engineering, product, and cross-functional teams while contributing to cloud-native applications, data pipelines, distributed systems, testing, deployment, and production operations.

Leads AI presales strategy, customer advisory, technical solution architecture, and team development. Translates business needs into AI use cases, prototypes, business cases, roadmaps, and repeatable offerings. Partners with sales, engineering, consultants, and technology vendors to support complex pursuits, executive briefings, demonstrations, proofs of concept, and AI business growth. Promotes responsible AI governance, tracks pipeline performance, develops thought leadership, and remains hands-on in high-impact customer engagements.

Lead and manage the underwriting and credit risk model validation team, perform hands-on independent validations of complex ML models, assess data integrity and performance, identify and remediate model weaknesses, partner on MRM framework improvements, and coordinate with audit, compliance, and controls to meet regulatory standards.

Drive new enterprise revenue and customer expansion by selling AI-powered SaaS solutions. Manage complex 6–12 month sales cycles, build executive relationships, develop value propositions, maintain pipeline accuracy, lead virtual account teams, and close high-value deals. Translate technical AI capabilities into measurable business outcomes, guide customer adoption, collaborate with marketing, product, and customer success teams, and exceed quarterly pipeline and revenue targets.

Own Sophos EDR strategy, roadmap, technical product development, AI-powered experiences, and go-to-market execution. Translate customer needs, threat research, telemetry, competitive intelligence, and business goals into product priorities. Partner with engineering, security operations, threat research, marketing, sales, and customer success to deliver endpoint detection, investigation, threat hunting, response, reporting, and XDR/MDR upgrade capabilities while driving adoption, revenue, and customer outcomes.

Architect and prototype scalable solutions for Twilio’s emerging technologies experimentation team. Lead AI, machine learning, and LLM-driven initiatives; guide technical direction; mentor engineers across the US and India; align prototypes with business goals; and ensure enterprise architecture, scalability, extensibility, and production readiness. The role requires extensive architecture or principal engineering experience, cloud expertise across AWS and Azure, full-stack development knowledge, database expertise, and strong technical leadership.

Leads digital transformation of procurement through technology, automation, and AI. Owns the source-to-pay technology roadmap, procurement platform strategy, tool implementations, KPI and value tracking, change management, training, governance, and adoption. Partners with Procurement, IT, Data & Analytics, Finance, suppliers, and global stakeholders to improve sourcing, supplier management, contracts, purchasing efficiency, cost optimization, and risk management.