Top Senior Machine Learning Jobs in Minneapolis, MN

Lead AI agent optimization efforts by designing systems that improve token efficiency, response time, reliability, and output quality. Build agentic coding systems, develop AI application patterns, own features end to end, collaborate with engineering and field teams, support the developer community, and create public technical documentation. Work across Python, TypeScript, Java, and Go while serving as a domain expert in agent design and optimization.

Design and productionize machine learning, deep learning, LLM, personalization, recommendation, search, and retrieval systems. Collaborate with product, engineering, and analytics teams to deploy AI solutions that improve customer experiences, business growth, and operational efficiency. The role requires driving projects from ideation through deployment and delivering measurable business impact in a fast-moving fintech environment.

Develop and optimize machine learning and statistical models for fraud detection and anti-money laundering. Analyze large datasets to identify fraudulent patterns, build scalable data pipelines and features, evaluate model performance, and collaborate with engineering, product, and operations teams. Communicate insights to technical and non-technical stakeholders while maintaining data privacy and security compliance. The role offers significant ownership in building Oscilar’s early machine learning stack within a remote-first startup.

Own LanceDB’s product direction by shaping the roadmap with technical leadership, researching users and competitors, gathering continuous user insight, and driving cross-functional prioritization. The role partners closely with engineering as a technically respected peer, defines product changes for AI and data-platform users, uses metrics and experiments to guide decisions, and operates with high ownership in an ambiguous environment.

Advise enterprise clients on AI and data strategies, transformation roadmaps, operating models, governance, privacy, technology rationalization, and business cases. Lead discovery workshops, executive engagements, and art-of-the-possible sessions; prioritize use cases and connect technical recommendations to business outcomes. Build the advisory practice, develop offerings, manage multidisciplinary teams, mentor staff, strengthen client and partner relationships, and support successful distributed project delivery.

Tune AML and fraud detection models using customers’ live transaction data. Investigate missed cases, alert quality, pattern drift, and false positives; validate changes through backtesting and sample review; document regulatory rationale; and present model performance and business value to compliance, risk, and data science stakeholders. Partner with customer teams on account strategy and relay customer-specific or systemic findings to product and model functions.

Drive net-new enterprise customer acquisition from lead generation through contract closure. Build C-level relationships, develop account strategies, manage CRM pipelines and forecasts, negotiate complex contracts, and close large AI, machine learning, and data talent engagements. Partner with marketing, customer success, and talent teams to deliver customer outcomes and expand Factored AI’s enterprise footprint.

Own the production lifecycle for machine learning and LLM-backed systems, including training and post-training pipelines, inference serving, model releases, monitoring, retrieval infrastructure, and cost and latency optimization. Partner with AI researchers and engineering teams to move prototypes into reliable production capabilities across customer tenants. Develop ETL and GraphQL features, enforce tenant isolation, and support deployments in regulated or customer-controlled environments.

Designs, deploys, and scales production AI agents and agentic workflows within enterprise Java and Spring applications. Establishes evaluation, monitoring, guardrail, safety, testing, observability, deployment, and operational standards for LLM systems. Leads architectural decisions across AI services and microservices, productionizes models, evaluates emerging technologies, mentors engineers, and influences technical strategy across teams.

Lead the design and implementation of large-scale AI and machine learning systems for Yahoo Search across multiple teams. Define data strategies, evaluation frameworks, and scalable production-serving approaches. Drive technical alignment among engineering, product, design, and research teams; guide adoption of large language models, vision models, generative AI, and agentic AI; and oversee data analysis informing strategic product decisions.

Build and operate scalable ML infrastructure and platform capabilities across cloud and on-premises environments. Develop Python-based developer tooling, services, and production systems supporting experimentation, distributed training, model deployment, and operations. Lead complex initiatives from architecture through rollout, improve reliability and developer productivity, and partner with ML and infrastructure teams. The role emphasizes Kubernetes, AWS, distributed compute, GPU-intensive workloads, observability, and production excellence.

You will tackle AI challenges, develop scalable software and collaborate with teams to enhance healthcare navigation using AI solutions.

Partner with functional leaders to translate business strategy into people strategy. Own org design, performance management, compensation, talent development, engagement, and retention. Provide coaching on career pathing, change management, and sensitive employee issues. Use people data to generate insights and recommendations, build compliant HR policies, manage global employee relations, and collaborate with People Team COEs to deliver cohesive people programs at scale.

Serve as second-in-command for Coinbase Derivatives' Market Regulation Department, leading day-to-day regulatory compliance, managing exams and audits, advising on CFTC Core Principles, reviewing surveillance escalations and enforcement actions, and coordinating cross-functional regulatory workstreams to ensure the DCM meets obligations and resolves findings timely.

Develop and maintain open-source Python frameworks and ML developer tools, architect scalable libraries, grow early-stage projects, build real-time streaming capabilities, integrate modern frontend technologies, and collaborate with open-source contributors and the broader developer community.

Drives operational excellence through data analysis, dashboards, process improvements, and cross-functional project leadership. The role embeds with business units to manage day-to-day operations, investigate trends, answer strategic questions, implement workflows, coordinate stakeholders, track deliverables, and present insights to technical and non-technical audiences. It serves as a strategic partner to operations leadership in a fast-paced, ambiguous environment.

Develop experimental AI techniques for agentic marketing applications, especially image and video generation. Build proofs of concept, autonomous evaluation and improvement systems, realistic AI-generated video, and brand-aligned creative generation workflows. The role requires strong quantitative and probabilistic thinking, backend architecture skills, creativity with LLM applications, and product intuition.

Drive MSSP partner growth across AMER by recruiting, onboarding, activating, and expanding strategic managed security service provider relationships. Develop territory recruitment plans, executive partnerships, joint business plans, and indirect routes to market. Help partners launch managed security services, generate pipeline, increase revenue, and accelerate time-to-revenue. Collaborate with sales, channel, marketing, services, and technical teams while managing forecasts, opportunities, executive reviews, and revenue objectives.

Own the end-to-end product direction and quality of an AI email application. Define AI and system requirements, translate model capabilities into product decisions, balance quality, cost, latency, reliability, safety, and user experience, and partner with ML, backend, and client engineers. Establish evaluation frameworks, quality standards, feedback loops, and failure recovery strategies while driving prioritization, execution, experimentation, and user trust in a zero-to-one environment.

Own the end-to-end product direction and quality of an AI-powered email application. Define AI and system requirements, evaluation frameworks, quality standards, and measurable outcomes. Partner closely with ML, backend, and client engineering teams to make trade-offs across quality, latency, cost, reliability, safety, and user experience. Drive prioritization, specifications, experimentation, failure-mode mitigation, and continuous iteration in a technical zero-to-one environment.

Lead the technical side of enterprise sales for autonomous systems and physical AI customers. Own discovery, architecture reviews, demonstrations, proofs of concept, evaluations, deployments, and expansions. Translate customer workflows into solutions, address technical objections, and collaborate with Product and Engineering. Build scalable sales engineering processes, technical content, evaluation frameworks, and operating models while supporting strategic automotive OEM and enterprise opportunities.

Lead product strategy, requirements, and roadmap development for Honeywell Forge cloud applications serving utilities. Own AI and analytics capabilities for grid operations, DER management, rate design, and utility programs. Conduct customer and market discovery, competitive analysis, and voice-of-customer research. Partner with solution architects, sales, customers, and engineering to prioritize and deliver B2B SaaS products that generate measurable utility operational value.

Lead product strategy, requirements, roadmap, and AI capabilities for Honeywell Forge cloud applications serving utilities. Conduct customer and competitive discovery, translate utility workflows into product investments, define commercial opportunities, and partner with engineering to prioritize and deliver cloud-native B2B software. The role focuses on AI for grid operations, DER management, rate design, and utility program performance while ensuring measurable operational value.

Lead technical direction for high-impact machine learning initiatives, from research and experimentation through production deployment. Build and fine-tune LLMs, document understanding systems, model-serving infrastructure, evaluation frameworks, and data feedback loops. Establish standards for model quality, calibration, reproducibility, and responsible deployment while mentoring ML engineers and influencing organizational roadmaps. The role requires hands-on software and ML engineering expertise, production-scale model development, and strong statistical judgment.

Own the global consumer engagement and personalization domain across digital channels. Assess current capabilities, define multi-year strategy and roadmap, recommend personalization infrastructure investments, and translate priorities into shipped products. Partner with engineering, data science, design, marketing, legal, and regional teams to deliver scalable experiences. Establish metrics, run cross-market experiments, optimize performance, and make global versus localized decisions. Influence executives, mentor product managers, and apply AI, identity resolution, and generative AI to personalization.