Top Remote Machine Learning Jobs in Kansas City, MO

As a Senior Fullstack Engineer, you'll architect and build a high-concurrency auction engine, leveraging AI for enhanced user experiences and system reliability.

Lead engineering strategy and delivery for customer-facing web and mobile platforms; drive AI-native transformation, increase conversion and speed-to-fund, raise engineering talent and velocity, ensure secure and compliant architectures, partner with product and cross-functional teams to deliver measurable business outcomes.

The Senior Data Scientist will develop scalable ML models, analyze large datasets for insights, and collaborate with teams to enhance business performance through data-driven solutions.

The SVP of AI Innovation - Web Scraping will lead the development of AI-driven web scraping strategies, focusing on data acquisition, technology enablement, compliance, team building, and cross-functional collaboration for new data services.

The Product Manager will identify process improvements and innovations, utilize automation and AI, and manage the product lifecycle to enhance productivity and deliver business value.

Design, optimize, and deploy deep learning models for customer behavior prediction. Perform feature engineering, implement research-informed architectures, scale training pipelines, collaborate with client teams, document findings, and uphold engineering best practices.

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.

Design, train, and deploy ML models for Qur'ān recitation, tajwīd detection, and ASR. Build data pipelines for cleaning, segmentation, labeling, and evaluation. Integrate real-time predictions via APIs or lightweight on-device inference, optimize models for speed and mobile performance, and collaborate with backend developers to ship AI features in the app.

Develop novel machine learning methods to enhance computer vision solutions, identify areas for innovation, and translate research into scalable production systems.

Lead design, build, and operation of end-to-end production ML systems (feature pipelines, training, inference, experimentation, monitoring) for ranking, recommendation, rewards, ROAS/LTV prediction, personalization, and offer optimization in a high-scale AdTech environment. Provide hands-on technical leadership, mentor engineers, drive MLOps and observability standards, and partner with Data Engineering, BI, Product, and business teams to connect models to outcomes.

The AI Application Engineer will design and deploy AI systems, collaborate with teams, implement Generative AI solutions, and promote continuous learning in AI practices.

Lead onboarding and training for new customers of an AI-powered construction SaaS. Customize training, run webinars and office hours, create documentation, drive user adoption, surface issues with support, and collaborate with product and customer success to refine onboarding processes.

Lead and operate a 10,000 sq ft micro-fulfillment lab to validate and scale warehouse automation, robotics, and AI-driven operations. Own strategy, budgeting, vendor selection, experimentation, and deployment frameworks to drive measurable productivity, capacity, accuracy, and cost improvements across Stord's fulfillment network. Build and mentor a multidisciplinary team and partner with internal and external stakeholders to commercialize solutions.

Lead strategy, roadmap, and delivery for Tekmetric's SaaS product for auto repair shops. Own user research, AI/ML product initiatives, cross-functional alignment, go-to-market coordination, and measurement of business outcomes. Train internal teams and iterate on features from concept through launch and optimization.

Lead product strategy and execution for data and AI platforms, driving GenAI/LLM initiatives, cross-functional collaboration, responsible AI governance, experimentation (A/B testing), measurement of model impact, and mentoring product teams to embed scalable ML capabilities into clinical and member experiences.

Lead design and production deployment of ML models focused on pricing, availability, forecasting, and optimization. Build data infrastructure, governance, testing/monitoring, and dashboards. Partner with executives and cross-functional teams, mentor others, and establish scalable data-science practices and experimentation frameworks.

Lead the development of deep learning models for email data, optimize models, design evaluation frameworks, integrate products, and mentor junior researchers.

Lead the development and implementation of machine learning solutions, managing a team to drive strategic decisions and innovative business outcomes.

Lead initiatives in designing and building large-scale distributed data systems and ML infrastructure, while collaborating across various engineering teams.

Own pre-sales technical engagement: consult customers, deliver technical presentations and demos (web, iOS, Android), lead POCs, support RFP/RFI responses, manage onboarding and integrations, and advise sales, product, and engineering teams on implementation and value realization.

Design and productionize ML models and agentic systems for intraday financial alpha generation. Build data pipelines, experiment frameworks, and portfolio optimization methods. Collaborate with quants and engineers to move models from research to live trading environments.

Develop and tune ML models, productionize and deploy strategies for ML systems, and align cross-functionally to communicate performance metrics.

As a Staff Machine Learning Engineer, you will develop, productionize, and deploy ML models for clinical use, ensuring real-world effectiveness through hands-on coding and infrastructure building.

Lead end-to-end ML system development: data exploration, model research, training, deployment, evaluation, and MLOps. Collaborate cross-functionally to prioritize claims, improve matching, measure business impact, and own a technical workstream.

The Staff Machine Learning Engineer will develop and maintain MLOps tools, optimize model performance, and collaborate on ML system architecture with a focus on enhancing developer experience.