Top Machine Learning Jobs in Kansas City, MO

Build and operate scalable cloud data pipelines, warehouses, transformation layers, and ML production systems. Lead data infrastructure architecture, warehouse performance optimization, DataOps monitoring, CI/CD improvements, data quality testing, and large-scale data product delivery. Collaborate across teams, promote data stewardship, and support batch and streaming pipelines while deploying, serving, and monitoring machine learning models.

Build backend services, APIs, data-processing systems, and frontend features for Northbeam’s marketing intelligence platform. Work on experimentation, near-real-time data infrastructure, AI-powered workflows, data integrations, RBAC, and end-to-end product delivery. Collaborate with data scientists, product managers, designers, and senior engineers while contributing across backend, frontend, pipelines, and infrastructure in a fast-moving environment.

Leads and builds Instructure’s decision science function, including hiring, team development, roadmap ownership, experimentation, causal and predictive modeling, executive insights, and data literacy. Partners with product, engineering, marketing, finance, and executives to guide product, pricing, retention, go-to-market, and operational decisions. Establishes analytical standards, supports data infrastructure, and ensures ethical, privacy-conscious use of student data.

Lead and coach the engineering team building AutoReview, an AI-powered mechanical design review product. Partner with Product on roadmap planning, feasibility, estimation, prioritization, and timelines. Maintain technical context to identify risks and dependencies, guide initiatives involving DFM checks, assembly analysis, and bulk workflows, and collaborate with applied ML teams. Establish clear ownership, support engineer autonomy, and help the team navigate evolving customer needs and AI technologies.

The Senior Data Scientist will analyze data to drive product strategy, optimize performance, and support product launches, collaborating with cross-functional teams.

Conduct applied machine learning research from problem definition through deployment, developing predictive models, LLM applications, retrieval systems, and agentic workflows. Build rigorous datasets, evaluation frameworks, uncertainty assessments, and monitoring processes. Collaborate with engineering, product, research, communications, and program teams to integrate validated models into reliable products, prioritize AI/ML opportunities, and measure impact across energy, climate, media, public participation, and organizational use cases.

Lead Amazon performance marketing strategy and execution, manage Sponsored Ads and DSP, collaborate with agencies and cross-functional teams, optimize campaigns and budgets, analyze performance with AMC/analytics, and measure commercial impact to drive ROAS and customer acquisition.

Build and deploy NLP and LLM-based machine learning models for an AI-powered risk intelligence platform. Responsibilities include text classification, entity extraction, clustering, topic detection, RAG, agentic workflows, semantic search, model evaluation, production monitoring, retraining, and scalable ML service deployment. Collaborate remotely with data engineers, backend engineers, and product stakeholders on high-volume, multilingual text data.

Owns US customer and commercial operations for a healthcare hardware and AI company. Responsibilities include forecasting camera demand, managing manufacturing partners and inventory, launching repair and replacement services, executing and negotiating customer contracts, managing legacy obligations, creating SOPs and policies, and automating operational workflows with AI tooling. The role is remote, US-based, with occasional travel for customer engagements and team offsites.

Own adoption, retention, and expansion across a pooled portfolio of scaled customers through digital-first programs. Provide technical API integration guidance, troubleshoot issues, run demos, and create reusable enablement assets. Design automated customer journeys, webinars, self-service resources, and workflows that accelerate activation. Monitor usage and health data, identify expansion opportunities, report KPIs, and build scalable systems that reduce recurring manual work.

Lead CX data intelligence initiatives using SQL, dashboards, analytical models, and AI/LLM techniques. Analyze customer journeys, operational metrics, churn risks, and retention indicators; develop insight agents and self-service reporting; define CX health metrics; and translate findings into strategic recommendations, process improvements, and experiments. Partner with operations, support, product, data, and engineering teams while maintaining data quality, governance, and documentation.

Design, train, and deploy scalable ML models (generative, classification, regression). Define feature roadmaps, collaborate with engineering to implement code and APIs, adapt ML methods for distributed/GPU environments, and create evaluation/annotation programs for fine-tuning and performance measurement.

Leads responsible AI and model governance while building and deploying healthcare fraud, waste, and abuse models. Establishes standards for documentation, monitoring, bias, drift, validation, and production readiness. Evaluates generative AI use cases, develops policies, validates large-scale claims models, and presents methodology and controls to partners, auditors, and investigators. Works remotely with data scientists, BI developers, and healthcare subject-matter experts.

Own and execute multi-year strategic partnerships with a hyperscaler and an AI lab, aligning technical integrations, commercial motions, and joint go-to-market plans. Coordinate cross-functional teams, run operational cadences, track measurable outcomes (pipeline, ARR, integrations), and represent partnerships to executives while driving hands-on execution and follow-through.

Build and maintain Safari AI’s cloud infrastructure, including production services, system design, reliability, documentation, and security compliance. The role sets technical direction for the cloud stack, collaborates with engineering and operations teams, improves development processes, and delivers incremental features. Candidates should have 4–6 years of software engineering experience, public cloud expertise, and experience taking complex services from concept through production. Streaming processing and machine learning experience are advantageous.

Design, develop, implement, support, and improve financial-system integrations, databases, reporting solutions, and automated processes. Lead technical financial systems projects from requirements gathering through implementation, focusing on Aderant Expert, SQL Server, SSIS, SSRS, and T-SQL. Collaborate with Finance, IS, vendors, and business stakeholders; troubleshoot complex data issues, optimize database performance, document technical solutions, and support multiple concurrent initiatives.

Conduct independent research on frontier AI evaluation and auditing. Design rigorous evaluations, audit methods, and reproducible experimental pipelines; analyze model capabilities, risks, robustness, and alignment-relevant behavior. Produce audit reports, recommendations, publications, tools, and presentations while collaborating with technical, policy, engineering, and standards teams. The role requires careful handling of confidential information and may involve pilot audits with frontier AI companies.

Own Litify’s AI Call Intelligence product line end to end, including call capture, transcription, summaries, conversational insights, voice agents, and agentic workflows. Define the roadmap, quality standards, guardrails, and evaluation practices; lead client discovery, beta programs, competitive research, backlog prioritization, and cross-functional delivery. Partner with engineering, design, data science, and go-to-market teams to launch products, drive adoption, and measure outcomes.

Drafts and prosecutes U.S. and international patent applications focused on quantum computing and emerging technologies. Responsibilities include working with inventors and engineers, preparing Office Action responses, managing patent portfolios, advising on patent strategy, and developing client relationships. Requires substantive quantum computing patent experience, USPTO registration, a technical degree, a JD, and at least three years of patent preparation and prosecution experience.

Leads Sophos’s Americas MSP channel strategy, revenue growth, partner productivity, and operating performance. Oversees recruitment, onboarding, enablement, retention, distribution relationships, recurring-revenue forecasting, strategic negotiations, and program optimization. Builds and coaches direct and matrixed teams while influencing product, pricing, investments, and partner-program decisions. Uses performance data and market intelligence to improve activation, adoption, retention, expansion, and return on investment across cybersecurity managed-service offerings.

Lead client-facing AI program governance and delivery as PMO manager. Translate business objectives into AI roadmaps and OKRs, oversee AI/ML lifecycle, ensure data readiness, model validation, deployment, and responsible AI compliance. Manage PMO standards, portfolio prioritization, AI-augmented reporting, and lead executive communications while mentoring PMs, TPMs, and analysts.

Own and scale the company’s global privacy and data protection program. Advise product, engineering, sales, marketing, security, and executives on privacy laws, data transfers, DPAs, AI governance, security commitments, incident response, and regulatory requirements. Manage privacy assessments, data inventories, notices, training, outside counsel, and vendor relationships. Partner with Security and GRC on audits, certifications, breach response, government requests, and customer obligations while building scalable processes and eventually a privacy team.

Designs and operates production data infrastructure, ETL/ELT pipelines, APIs, containerized AWS services, and graph-based systems. Responsibilities include relational, document, and graph database architecture; data quality and lineage; CI/CD automation; and integration of blockchain, financial, and investigative data. Partners with R&D, data scientists, and investigations teams to deliver production software for federal and commercial clients.

Lead the Personal Loans portfolio strategy, quantitative analysis, risk-based pricing, deal economics, and profitability initiatives. Manage a distributed analytics and portfolio team across New York and Israel, align strategies with partners, and collaborate with Risk, Product, Capital Markets, and Engineering. Translate complex credit and financial analysis into executive recommendations while optimizing customer conversion, risk-adjusted yield, margins, and lifecycle outcomes.

Lead a portfolio of technology-enabled continuous improvement initiatives for internal operations and DoD customers. Manage product backlogs, write user stories, define roadmaps, gather requirements, and facilitate Agile, Scrum, SAFe, and PI planning activities. Drive Splunk analytics, dashboarding, alerting, AI, ML, automation, proactive monitoring, and predictive analytics solutions. Ensure stakeholder alignment, measurable operational improvements, and compliance with DoD security requirements while maintaining an active Secret clearance.