Top Remote Machine Learning Jobs in Kansas City, MO

Lead the roadmap and delivery of an AI data platform supporting internal teams and external healthcare products. Manage ML and LLM platform workstreams across data ingestion, training, deployment, monitoring, and workflow optimization. Partner with AI engineers, researchers, data scientists, clinical leaders, and business stakeholders to define requirements, develop user stories, validate model performance, and turn proofs of concept into scalable production capabilities.

Provide legal support for commercialization and product development of an AI-powered ad tech platform: draft and negotiate commercial agreements, manage IP portfolio and complex IP litigation, advise on AI, data, and privacy matters, counsel product teams on compliance and IP protection, and develop automated legal workflows.

Leads client services and cross-functional delivery teams supporting enterprise software implementations. Oversees end-to-end project execution, client relationships, business-process transformation, stakeholder education, requirements documentation, and ongoing value delivery. Applies revenue growth management expertise across pricing, trade promotions, assortment, or supply planning, while collaborating with technical teams on AI, machine learning, and analytics solutions.

Own product marketing for Gladly’s Agent Harness and AI Core platforms. Develop technical positioning, messaging, launch strategies, sales enablement, competitive intelligence, and narrative assets. Translate AI agent behavior, evaluation, steering, and measurement into credible messaging for CX leaders and technical buyers. Manage tier-one launches end to end while balancing two product areas as a senior individual contributor with minimal oversight.

Leads and develops a perception and machine learning engineering team building computer vision, sensor fusion, tracking, detection, and scene-understanding capabilities for autonomous systems. Owns roadmap planning, technical decisions, delivery, model development and deployment workflows, field readiness, hiring, performance management, and cross-functional execution with autonomy, embedded, data, hardware, product, programs, and field operations teams.

Develops and optimizes Azure HPC and AI platform software supporting GPU-based training and inference workloads. Responsibilities include hardware/software integration, device virtualization, performance analysis, debugging, coding, architectural design, on-call support, issue resolution, and cross-team collaboration. The role improves scalability, reliability, test coverage, and performance across Azure infrastructure and AI training services.

You will execute and improve insurance workflows, analyze operational efficiency, collaborate with product teams, and support onboarding and documentation efforts.

Develop and deploy machine learning models for manufacturing use cases including predictive quality, machine failure prediction, and time-series pattern discovery. Work directly with customers to scope problems, validate solutions in live deployments, and translate successful analyses into product features. Collaborate with engineering, product, and customer success teams while applying statistics, optimization, generative AI, and agentic AI to deliver end-to-end solutions.

Own the end-to-end integration and automation architecture for the MyCDW platform. Design ServiceNow integrations, APIs, event-driven patterns, middleware, data contracts, AI-agent guardrails, automated workflows, and observability. Establish governance, security, coding standards, testing, documentation, and reusable architecture patterns. Evaluate platforms, lead prototypes, resolve complex issues, manage vendors, influence stakeholders, and align solutions with business strategy, compliance requirements, scalability, resilience, and cost objectives.

Analyzes complex healthcare data to identify trends, develop statistical and machine learning methodologies, and produce actionable insights. Builds dashboards and reports, supports product and business decisions, promotes data literacy, and collaborates with cross-functional teams. The role also ensures analytics practices comply with healthcare privacy, governance, security, and regulatory requirements, including HIPAA.

Leads commercial growth execution as the CCO’s operating partner, orchestrating go-to-market programs across Sales, Marketing, Customer Success, Partnerships, and Operations. Establishes execution frameworks, operating rhythms, accountability systems, KPI dashboards, and performance reporting. Aligns cross-functional leadership, scales sales motions, identifies capability gaps, removes execution barriers, and drives measurable revenue, customer, and market growth initiatives.

Owns product vision, strategy, roadmap, and execution for Yahoo Search monetization across traditional and AI-powered search. The role optimizes sponsored search relevance, ranking, placement, coverage, revenue, and user experience while developing new advertising formats for conversational and generative experiences. Responsibilities include experimentation, marketplace analysis, performance measurement, partner collaboration, and close work with engineering, data science, analytics, and design teams.

Build and maintain mission-critical wealth management systems for SMB clients. Design data pipelines, ETL/ELT processes, data models, warehouses, analytics, and reporting solutions using AWS, Snowflake, Mage AI, and other big data tools. Collaborate with data scientists and analysts on predictive models, statistical analyses, and machine learning algorithms. Develop software with Next.js and Python while working closely with the team and clients on financial-domain challenges.

Lead end-to-end data science and AI projects: translate business problems, prepare large datasets, build and deploy predictive/prescriptive and generative AI models (including LLM fine-tuning and prompt engineering), communicate insights to stakeholders, and promote responsible, explainable AI practices across consulting engagements.

Design and implement enterprise AI architectures spanning GenAI, machine learning, RAG, agentic workflows, data pipelines, APIs, and production deployments. Build Python prototypes and AI services, define governance and evaluation frameworks, integrate models with enterprise platforms, and guide engineering teams through implementation. The role requires hands-on expertise with LLM applications, orchestration frameworks, model lifecycle management, monitoring, security, and scalable production AI systems, preferably in financial services.

Design, build, and maintain optimization technology, full-stack systems, and data-intensive scheduling infrastructure. Improve optimization algorithms using linear optimization, machine learning, and generative AI. Lead complex technical initiatives, establish testing and observability, support scalable operations, troubleshoot customer issues, participate in on-call rotations, and mentor engineers while partnering closely with product and engineering teams.

Develop and validate machine-learning models for drug discovery using chemical, biological, imaging, phenotypic, and structural datasets. Perform exploratory analysis, integrate heterogeneous data, build reproducible workflows, and communicate findings to medicinal chemists, biologists, and research scientists. Apply supervised, deep-learning, graph-based, and ensemble methods to support compound optimization, target discovery, screening, and translational research.

Build and operate Lakehouse data pipelines, product features, machine learning and generative AI components, testing, monitoring, instrumentation, and documentation. Collaborate with product managers, engineers, and business users to clarify requirements, deliver production solutions, investigate defects, and improve reliability and performance. Participate in code reviews, technical design discussions, and backlog-driven development while following engineering standards and contributing to scalable Data & AI products.

Own sales analytics and forecasting as the trusted source of truth for sales leadership. Build predictive and machine learning models to assess deal close likelihood and slippage, maintain Salesforce and Tableau dashboards, improve data governance, and translate complex business questions into actionable GTM recommendations. Partner with Sales, Marketing, and Finance on reporting standards, strategic planning, QBRs, and executive insights.

Build and deploy production generative AI and machine learning systems for utility clients. Own engagements end to end, including discovery, architecture, retrieval, model selection, evaluation, deployment, monitoring, operator-facing applications, and post-go-live support. Integrate with client data and business systems, establish AI safety and governance controls, manage scope and risk, and communicate with technical teams and executives. Contribute reusable accelerators, evaluation frameworks, and reference architectures across client engagements.

Own Autopilot, Blend’s AI agent product for mortgage and lending operations, as a business and product roadmap. Prioritize capabilities, define evaluation and quality frameworks, establish pricing and packaging, productize customer-specific solutions, and measure business impact. Partner with AI engineering, forward-deployed teams, customers, and go-to-market leaders to drive adoption, ensure compliance and auditability, and translate production learnings into scalable offerings.

Leads strategic partnerships and new ventures from initial concept through signed agreement and scaled commercial launch. Identifies whitespace opportunities, develops business cases, structures and negotiates complex enterprise deals, builds financial models, and presents propositions to executive stakeholders. Coordinates Finance, Legal, Engineering, Product, Operations, and Commercial teams to validate economics, technical feasibility, funding, resources, and execution plans. The role is remote across the US and Canada with regular travel.

Leads product strategy, roadmap, and execution for threat intelligence, threat hunting, and detection capabilities. Partners with engineering, architecture, security research, executive, sales, and SOC teams to drive innovation, product launches, OKRs, portfolio prioritization, and customer security outcomes. Requires deep cybersecurity expertise and extensive senior product management experience, with strong knowledge of emerging threats, AI applications, and enterprise security products.

Leads strategy and execution for data products powering threat intelligence solutions. Uses customer insights, big-data analysis, and competitive research to prioritize opportunities, refine API-first products, and integrate data into UI, AI, and third-party workflows. Collaborates with engineering, marketing, sales, and solutions engineering from ideation through launch, influencing stakeholders and executives through data-driven analysis. Requires substantial cybersecurity or data product management experience and expertise in threat intelligence, APIs, analytics, and customer ecosystems.

Leads ExtraHop’s product marketing function, including team leadership, portfolio positioning, messaging, product launches, competitive analysis, sales enablement, content development, integrated campaigns, and executive-level customer and industry engagement. The role requires deep cybersecurity and enterprise technology expertise, strong storytelling and technical communication skills, and collaboration across product, sales, marketing, and partner teams.