Top Senior Machine Learning Jobs in Minneapolis, MN

Design, productionize, and deploy machine learning forecasting models for ad inventory and campaign performance in low-latency, real-time advertising environments. Build scalable simulation, budgeting, and pacing systems; collaborate with cross-functional teams to deploy models at scale and optimize ad monetization.

Lead design, execution, and optimization of scaled lifecycle campaigns across email and in-product channels. Use marketing automation, behavioral data, and ML techniques to personalize journeys, drive activation, revenue, retention, and cross-sell. Analyze performance, generate insights, and collaborate with Growth, Product, Engineering, Data Science, Analytics, Sales, and Marketing stakeholders.

Lead design, execution, and interpretation of A/B tests and quasi-experiments; apply causal inference methods; partner with product, engineering, and marketing; mentor data scientists; build experimentation frameworks; extract and analyze data using SQL and Python; communicate results to stakeholders.

Design, build, and deploy scalable AI/ML models and pipelines for consumer healthcare. Develop production code (Python/Java/Scala), automate infrastructure (Terraform, Kubernetes), validate model performance, integrate cloud services (Azure/AWS), and collaborate cross-functionally to monitor and document enterprise-scale AI solutions.

Design, build, and deploy scalable data pipelines and AI/ML solutions using LLMs and agentic frameworks. Implement prompt engineering, fine-tune models, integrate AI workflows in cloud environments, ensure healthcare compliance and Responsible AI, and mentor junior engineers to deliver automated claims adjudication and other production AI systems.

Lead integration of state-of-the-art AI/ML into Netflixs content platform and publishing tools. Build roadmaps, incubate ML systems from idea to production, partner with researchers and engineers, automate promotional asset creation, optimize distribution and localization, and measure impact with data-driven evaluation.

The Senior Product Manager will set product direction, own the development lifecycle, collaborate with teams, and ensure high-quality outputs. This role requires strong opinions on product quality and customer insights.

Lead the Decision Science function, designing ML models, overseeing experimentation frameworks, and ensuring model adoption across the company, driving operational and commercial improvements.

Lead and build a team that drafts and submits state-specific rating rules, underwriting guidelines, and forms; manage filing execution via SERFF, handle objection responses, coordinate cross-functional stakeholders, establish operating rhythms, improve documentation and controls, and coach staff to scale filing expertise and regulatory execution.

Lead net-new enterprise sales into Fortune 500 and large mid-market accounts building AI/ML systems. Prospect and create outbound pipeline, run long consultative sales cycles, orchestrate pilots and phased rollouts, influence technical and executive stakeholders, and partner with solution architects to close complex, high-value deals.

Author and verify rigorous multiple-choice computer science assessment items across many CS domains to create gold-standard benchmarks for AI research. Produce correct answers, nine plausible distractors, solution explanations, difficulty labels, and authoritative references. Review and edit questions for technical accuracy, clarity, and reproducibility. Contribute asynchronously as an independent contractor, ~10+ hours/week, fully remote.

Design, build, and productionize AI systems (genAI, agents, RAG) with observability, model quality, governance, and responsible-AI controls. Implement serving/inference, cloud deployments, model registry/experiment tracking and cross-functional leadership to scale claim-edit automation and agentic workflows.

Lead a full-stack engineering team building, deploying, and operating production-grade AI/ML applications (LLMs, deep learning, agentic systems). Drive technical architecture, infrastructure, data, APIs, reliability, security, and ML Ops. Coach and develop engineers, align stakeholders, manage roadmaps, and ensure scalable, maintainable solutions that deliver measurable business value.

Lead product strategy and execution for an AgTech SaaS platform, driving grower workflows, data integration, analytics, IoT partnerships, and commercialization. Own roadmap, write requirements, collaborate with engineering/data science, and measure adoption, revenue retention, and platform usage.

As a Manufacturing Automation Engineer IV, you will lead the design and implementation of automated manufacturing systems, optimize processes, and collaborate with teams to enhance efficiency and quality.

Serve as primary client contact for a portfolio of loyalty-program clients. Scope and manage end-to-end actuarial analyses rooted in predictive modeling and machine learning, validate model outputs, translate results into clear recommendations and presentations, and partner with analysts and data scientists to deliver high-quality, client-facing work in a fully remote consulting environment.

Lead sales operations for seller acquisition at a marketplace: design pipeline and lead flows, remove onboarding friction, build SOT dashboards and forecasts, track listing health and KPIs, design incentive and quota programs, own CRM and sales enablement, and implement AI-driven automation and predictive analytics to scale seller and listing growth.

Partner with a retained search firm to place software engineers and quant developers at top hedge funds. Provide candidate flow from your network; firm supplies clients, process, and fees. Remote, commission-only role with placement and pipeline incentives. Apply with placement examples, outreach metrics, and a candidate submission.

Lead and align ML, data, and engineering teams to define multi-year AI roadmaps, deliver production ML solutions, ensure robust data pipelines, set success metrics tied to business outcomes, and grow/mentor senior managers and engineers.

Lead and build a data science organization to create a prediction engine for vendor matching, order routing, and supply chain optimization. Anchor projects to P&L, design measurement and experiment frameworks, ship rapid wins, hire and coach data scientists, work hands-on in modeling and technical design, partner with product and engineering (Snowflake), and evolve systems toward agentic AI while reporting impact in dollar terms.

Build, ship, and own end-to-end ML models for vendor matching, order routing, and supply-chain optimization. Partner with data engineering to productionize models, quantify impact, document work, and implement decision logic components toward agentic AI in a fast-moving healthcare startup environment.

Own end-to-end modeling for vendor matching, order routing, and supply-chain optimization. Build predictive, causal, RL and OR solutions; design autonomous decision logic; quantify counterfactual impact; ship production models with data engineering; mentor senior data scientists; write technical design docs; partner on quarterly priorities and expand into revenue cycle and finance domains.

Lead and grow an AI/ML engineering team to design, build, and deploy production-grade ML/AI solutions (NLP, NLU, deep learning, CV, ASR). Oversee architecture, MLOps, experiments, model monitoring/versioning, cross-functional collaboration, and ethical AI practices to translate research into scalable business solutions.

Design, develop, and deploy agentic AI workflows and LLM-based automations for clinical and business use. Build scalable RAG solutions, vector DB integrations, and production-grade Python services. Implement AI orchestration frameworks, ensure MLOps/LLMOps, monitor observability, and collaborate with product, clinical, security, and engineering teams to deliver secure, compliant healthcare AI capabilities.

Design, build, and deploy production-grade AI/ML models, agentic workflows, and data pipelines using Python, Databricks, LangChain/LangGraph, and Azure. Collaborate with cross-functional teams to translate requirements into scalable LLM applications, implement feature engineering and validation frameworks, and embed responsible AI practices including fairness, security, and monitoring.