Top Remote Machine Learning Jobs in Oklahoma City, OK

Lead the design and implementation of ML training systems, manage a team, promote engineering culture, and drive product development.

The Research Engineer will collaborate with scientists to develop and validate machine learning models for speech technologies, manage scalable training systems, and design tools for accessibility to non-technical users.

As a Senior AI Engineer, you'll design and implement AI-driven solutions, focusing on full-stack development and production-grade applications while mentoring junior team members.

Lead planning, deployment, tuning, and operational support of CrowdStrike Falcon's AI/ML detection and response capabilities. Implement and optimize ML models, develop automation playbooks and Falcon API integrations, troubleshoot AI-related issues, support incident response and threat hunting, and deliver customer workshops and collateral. Up to 25% travel.

Lead data science initiatives to develop and deploy AI solutions that improve business outcomes. Collaborate with cross-functional teams to analyze data, create models, and evaluate performance.

The Analytics Lead will leverage data analytics to optimize credit strategies, monitor portfolio performance, and collaborate on credit policy with stakeholders across technical and non-technical teams.

The Staff ML Engineer will design and operate Samsara's ML platform, collaborating with teams to enhance ML features and improve safety outcomes. Responsibilities include overseeing system reliability, leading technical direction, and mentoring engineers.

Lead the Creative Serving engineering team to define strategy, roadmap, and execution. Translate business goals into technical deliverables, collaborate with ML and product stakeholders, ensure high-uptime integrations with partner exchanges, provide technical guidance and feedback, enforce engineering best practices, balance short- and long-term priorities, and mentor engineers into future leaders.

Lead Pluralsight's curriculum strategy to identify and shape future skills; translate market and technology trends into a curriculum roadmap; align with product, GTM, and content production; measure portfolio performance with data; lead and develop curriculum directors and managers; define scalable frameworks and represent the company externally.

The Engineering Manager, Data Science will lead a team to drive machine learning initiatives in AdTech, focusing on optimization and modeling strategies to enhance client success and operational efficiency.

Lead product analytics to inform strategy and optimize user experience: translate business questions into analysis, build reliable analytics infrastructure, run A/B tests and ML/statistical analyses, track funnels and cohorts, support 0-to-1 product launches, and communicate actionable insights to cross-functional teams.

Build and validate machine learning and statistical models on large structured and unstructured datasets. Apply data mining, distributed processing (Hadoop/Spark/Kafka), and visualization tools to generate insights and integrate model outputs into production systems to support business decisions in financial services.

Lead the development of fraud prediction models using machine learning, collaborating across teams to build, scale, and monitor models in production.

Lead a team of ML engineers to develop fraud detection models, defining strategies, driving model evolution, and ensuring effective integration with cross-functional teams.

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.

As a Manager of Cloud Software Engineering at Coupa, you will lead AI platform DevOps, manage global teams, and oversee agile delivery while ensuring technical quality and innovation.

Lead and transform the Fraud Intelligence Team into a high-impact function by improving operating models, QA, tooling, and metrics; build entry-level talent programs; connect labeling to measurable fraud prevention outcomes; deliver actionable insights to Product, R&D, Analytics, and GTM; and leverage AI and automation to increase productivity and business impact.

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

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.

Own product strategy and delivery for chat, voice, and proactive support surfaces to maximize automation rates and CSAT. Build AI-integrated capabilities with ML/LLM teams and vendors, partner with Engineering, CX Operations, Data Science, and Compliance on platforms (Amazon Connect, Sprinklr, Salesforce), drive build-vs-buy decisions, and execute cross-functional delivery to optimize customer and agent experiences.

Lead strategy and delivery for an AI-powered claims decision-support and auto-adjudication platform. Define vision and roadmap, manage a team of product managers, partner with business, engineering, and data science, and drive adoption, impact measurement, and executive-level communication across a complex, matrixed enterprise.

Use advanced analytics and ML to design and monitor credit risk models, optimize underwriting and credit strategies, partner with Engineering/Product/Compliance, oversee portfolio performance and regulatory alignment, and lead cross-functional initiatives to balance growth, compliance, and risk mitigation.

As a Staff Machine Learning Engineer, you will design AI products on Edge devices, optimize ML model performance, and work with large-scale datasets, collaborating across teams to deliver impactful solutions.

As an ML Ops Infrastructure Engineer, you will design CI/CD pipelines for ML, maintain deployment systems, and implement monitoring while collaborating with research teams to ensure model quality and performance.

As a Systems Architect, you'll define and drive the infrastructure architecture for AI/ML workloads, design multi-cloud strategies, manage compute orchestration and storage systems, and lead capacity planning, while also collaborating with engineering leadership.