Top Senior Machine Learning Jobs in Minneapolis, MN

Lead CrowdStrike Finance & Accounting AI transformation: define operating model and governance, prioritize AI use cases, oversee POC-to-production delivery, drive enablement and change management, measure benefits realization, and report outcomes to executives.

Leads technical pre-sales and foundational implementation engagements for SailPoint SaaS solutions. Discovers customer requirements, maps them to product capabilities, delivers proof-of-value implementations, explains architecture and best practices, and collaborates with sales, product, services, and customer success teams. Develops AI and agentic technology solutions for demonstrations and customer problems, while serving as a technical and consultative advisor to customers, partners, and colleagues.

Lead technical customer engagements for strategic accounts, supporting sales, solution design, foundational implementations, and proof-of-value deployments. Discover requirements, map them to SailPoint solutions, educate customers on architecture and best practices, collaborate across sales, product, professional services, and customer success, and develop AI- and agentic-technology solutions for demonstrations and customer outcomes.

Forward Deployed Engineers support sales and customer engagements by discovering requirements, designing and implementing foundational SaaS solutions, delivering proof-of-value implementations, and educating customers on architecture and best practices. They collaborate with sales, product, professional services, and customer success teams; use AI and agentic technologies to solve customer problems; lead technical engagements; document requirements; and develop reusable demonstration solutions. The role may require up to 15% travel.

Forward Deployed Engineers support enterprise sales and customer engagements by discovering requirements, mapping them to SailPoint solutions, and delivering foundational implementations and proof-of-value projects. They provide technical leadership, explain architecture and best practices, collaborate across sales, product, services, and customer success teams, and develop AI- and agentic-technology solutions for demonstrations and customer problems. The role requires strong client communication, software implementation expertise, SaaS architecture knowledge, and familiarity with enterprise infrastructure and identity governance.

Leads the Cyber Threat Intelligence function and a team of cyber threat analysts. Responsibilities include analyzing emerging threats, advising senior leaders, integrating internal telemetry with external intelligence, automating intelligence workflows, and partnering with security engineering, incident response, SIEM, SOAR, and EDR teams. The role also drives threat prioritization, attribution, collection, correlation, strategic planning, and development of cybersecurity talent.

Leads a data science team focused on customer intelligence and personalization across digital products, apps, newsletters, and marketing channels. Develops lifetime value, segmentation, retention, recommendation, attribution, forecasting, and engagement models. Owns product funnel analytics and experimentation, including A/B testing and causal inference. Partners with Product, Marketing, Editorial, Data Operations, and Engineering to translate customer behavior into product improvements, measurable growth, and scalable data capabilities.

Analyze and characterize conversational audio data, identify underrepresented areas, and build active-learning and human-in-the-loop workflows. Develop representative benchmarks, automate model adaptation, and create reusable data analysis and scoring systems. Partner with research, engineering, and data teams to run experiments, improve models, organize data, and translate complex findings into measurable product improvements.

Lead the In-Vehicle Experience team at Samsara, focusing on transforming in-cab cameras into intelligent companions. Collaborate with engineering, prioritize features, and ensure client interactions highlight trust and connection. Conduct customer research and integrate AI and ML into product features.

Lead independent validation and ongoing monitoring of complex financial and analytics models, including ALM, loss forecasting, loan transition, corporate finance, and engagement models. Identify weaknesses, communicate findings, guide remediation, and strengthen model governance. Collaborate with Finance, Quantitative Research, Growth Analytics, Internal Audit, Internal Controls, Accounting, and Compliance to maintain Affirm’s model risk framework and address audit, regulatory, and examiner requests.

Lead analytics for Medicaid quality programs: build analytical frameworks, perform data analyses and causal inference to evaluate program impact, translate requirements into scalable data models, collaborate with cross-functional teams, and communicate findings and risks to stakeholders to drive quality improvements.

Partner with healthcare clients to design, prototype, and deploy production-grade AI/LLM solutions. Lead discovery, architecture, cloud integration, and delivery while troubleshooting distributed systems and driving measurable client outcomes.

Own Secret Detection and Vulnerability Research as a product: define strategy across prevention, detection, validation, revocation, and reporting; ship detection content and intelligence with measurable quality; drive adoption, retention, and revenue; partner with engineering, research, and Field; use AI to accelerate research and productization.

Lead teams building computer vision and machine learning for AI dash cameras, managing model lifecycle, coordinating with cross-functional teams, and driving AI product development.

Leads technical support for VA healthcare AI citizen developers, developing and validating supervised, unsupervised, and reinforcement learning models. Builds synthetic healthcare data cohorts, designs secure cloud-based data science workflows, collaborates with federal stakeholders, interprets analytical results, mentors developers, and ensures model quality, documentation, privacy, governance, and regulatory compliance.

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.

Owns CareSource’s EDI platform vision, roadmap, backlog, and delivery. Translates healthcare, regulatory, and integration requirements into technical specifications; partners with engineering, architecture, QA, DevOps, trading partners, and business teams; coordinates releases and Agile delivery; monitors transaction performance, defects, SLAs, and platform telemetry; drives data quality, observability, automation, compliance, documentation, and continuous improvement across EDI capabilities.

Leads the design and implementation of enterprise observability, AIOps, distributed architectures, cloud platforms, and software engineering solutions. Evaluates emerging technologies, guides architecture roadmaps, manages vendor governance, leads modernization and migration initiatives, and develops automation to improve system reliability and reduce detection and resolution times. The role also manages and coaches geographically distributed engineering teams while partnering with executive, cloud, SRE, and platform stakeholders.

Build and scale an international robotics data acquisition network across emerging markets. Source venues and strategic local agencies, optimize lead-generation funnels, evaluate deployment environments, coordinate hardware installation and calibration, and establish reliable data pipelines. Manage vendor training, SLAs, KPIs, verification, and anti-fraud controls while translating robotics and embodied AI data requirements into field operations. Requires international growth or field operations experience, partner management, technical data fluency, language skills, and frequent travel.

Leads the development and ownership of Cedar’s production machine learning personalization engine. Responsibilities include ML modeling, MLOps, feature pipelines, deployment, inference, monitoring, feedback-loop optimization, compliance and fairness guardrails, technical leadership, code reviews, cross-functional collaboration, and management of ML engineers. The role requires hands-on contributions to scalable ML systems serving millions of healthcare transactions while guiding roadmap execution and model improvement.

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.

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.

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 design, build, and deploy scalable, reliable Java-based microservices in a cloud-native, event-driven AWS environment. Own backend feature design, code quality, automated testing, and FedRAMP release processes. Mentor engineers, participate in on-call/TechOps, integrate AI tools into development, and drive architecture, observability, and cross-team initiatives.