Top Remote Machine Learning Jobs in Oklahoma City, OK

Designs end-to-end architectures and high-level designs for web security products and features, spanning configuration, reporting, runtime systems, analytics, and attack detection. Collaborates with product management and cross-functional teams to define requirements, guides engineering implementation, presents designs, and contributes security product expertise and technical roadmap direction.

Develop technical AI and data solutions for aerospace engineering applications. Responsibilities include designing architectures, building proof-of-concepts, coding and debugging applications, developing machine learning systems, presenting solutions to stakeholders, and shipping cloud-based products to production. The intern will gain experience with data engineering, cloud platforms, databases, web design, AI technologies, and numerical methods while collaborating across teams.

Develop and demonstrate AI and data solutions for aerospace engineering projects. Responsibilities include designing architectures, building proof-of-concepts and machine learning systems, writing code, creating demos, supporting cloud and data engineering initiatives, and delivering production-ready solutions. The intern will collaborate cross-functionally, participate in design thinking, gain mentorship, and work on real-world projects using technologies such as Python, cloud platforms, machine learning, LLMs, and computer vision.

Leads customer engagements and large-scale analytics projects from inception through delivery, ensuring measurable business value, quality, budgets, and timelines. Builds customer relationships, translates needs into analytics solutions, supports renewals and expansions, and coordinates with product, engineering, and data science teams. Also supports solution architecture, pre-sales demonstrations, proof-of-concept implementations, technical use-case development, and customer conferences. Presents complex insights to technical and nontechnical stakeholders.

Lead CluePoints’ alliance and channel strategy across technology partners, systems integrators, consultancies, and regional resellers. Build partner-sourced revenue, develop channel programs, enable partners, manage complex commercial relationships, and collaborate with Sales, Marketing, Product, and Services. The role will establish partnerships in markets including Japan, India, and Europe, while eventually building and leading a partnerships team.

Leads portfolio planning and cross-functional execution for Data Science Core and GenAI Studio programs. Establishes roadmaps, operating rhythms, priorities, dependencies, risk management, readiness criteria, decision processes, and success measures. Coordinates data science, engineering, product, research, privacy, legal, security, and business stakeholders from problem framing through deployment, adoption, and monitoring. Communicates technical tradeoffs and responsible AI considerations while improving team effectiveness, capacity planning, documentation, and delivery practices.

Create, solve, review, and validate coding-heavy machine learning tasks for AI training. Build and optimize models, training and inference workflows, data pipelines, evaluation systems, and reproducible Python workflows. Diagnose numerical and system-level failures, improve memory use and throughput, and verify technical correctness while working with coding agents.

Leads quantitative and qualitative market research on international development, AI adoption, institutional demand, funding, procurement, competition, and commercial opportunities. Evaluates AI and data service applications, develops market assessments and strategic recommendations, manages research initiatives, prepares executive presentations, and collaborates with leadership, technical, operational, and business development teams to support market entry and growth.

Own Fetch’s on-platform advertising measurement strategy, including event taxonomies, attribution, incrementality, reporting, and data pipeline standardization. Lead migration to standardized ad event infrastructure, define measurement methodologies such as multi-touch attribution and geo lift, and partner with engineering, data science, advertisers, and third-party measurement providers. Establish scalable measurement standards and ensure new ad products are instrumented and attributable from launch.

Lead AI/ML initiatives for Air Force condition-based maintenance, from predictive model development through production deployment. Architect data pipelines, develop machine learning, deep learning, NLP, and generative AI solutions, and build knowledge graph capabilities. Provide technical leadership, integrate models into applications and workflows, and monitor data quality, model performance, drift, and bias. Collaborate with engineering, cloud, software, and product teams while supporting defense aviation logistics and sustainment missions.

Develop AI agents, prompts, workflows, contextual data layers, orchestration frameworks, and machine learning models. Test and optimize solutions using structured and unstructured data, evaluate accuracy and scalability, analyze edge cases and failure modes, and document experiments and model performance. The 10-week remote internship is intended for students at least in their third year studying data science, AI, computer science, or a related field.

Manages global pharmaceutical quality systems, including eQMS implementation, maintenance, validation, enhancements, and lifecycle management. Optimizes quality workflows such as CAPA, change control, deviations, batch review, inspections, and audits. Develops quality metrics, reports, dashboards, and data governance practices while supporting digital transformation, automation, and AI initiatives. Partners with Quality, IT, analytics, regulatory, manufacturing, clinical, and other stakeholders on requirements, process improvement, system adoption, training, and change management.

Leads data science and advanced analytics initiatives for Auto Claims by developing machine learning and predictive modeling algorithms, integrating structured and unstructured data, and creating tools to support business decisions. Communicates modeling results to stakeholders, researches emerging techniques, manages moderately complex projects, and provides mentorship to junior data scientists. The role is remote with up to 10% travel to regional offices.

Own research and implementation of post-training methods for legal language models, including supervised fine-tuning, reinforcement learning, reward design, verifiers, preference modeling, evaluations, and distributed training. Lead experiments from research direction through production deployment, while debugging training instability, reward hacking, and divergence. Build rigorous evaluation systems and production-quality Python code for long-horizon legal reasoning and agentic workflows.

Build synthetic data pipelines, evaluation systems, benchmarks, verifiers, rubrics, and quality metrics for legal AI models and agentic workflows. Partner with researchers, legal experts, and customers to investigate model failures, validate metrics, maintain data quality and provenance, and convert expert feedback and product usage into training signals and improved evaluations.

Leads enterprise Key Drivers Analytics and an Associate Director team delivering advanced analytics across pharmaceutical business priorities. Owns analytics roadmaps, methodologies, governance, modeling standards, talent deployment, and globally distributed delivery. Directs multivariate analyses, segmentation, diagnostics, scenario modeling, validation, insight generation, and executive recommendations. Partners cross-functionally to translate evidence into measurable business actions while ensuring reproducibility, compliance, responsible AI, and scalable analytical solutions.

Leads enterprise advanced analytics and modeling to identify drivers of business performance across brands, markets, customers, accounts, and geographies. Translates findings into prioritized actions, playbooks, launch strategies, and scenario-based recommendations. Oversees rigorous, scalable analytical approaches, partners with senior stakeholders, and develops analytics talent across the organization.

Leads the enterprise Advanced Analytics and Modeling portfolio, strategy, roadmap, governance, investment priorities, and global operating model. Oversees analytics capabilities spanning precision health, commercial analytics, optimization, AI, NLP, GenAI, and unstructured intelligence. Partners with senior business and technology leaders to translate strategic decisions into validated, interpretable, reusable analytical solutions and measurable impact. Manages senior capability leaders and distributed US-Hyderabad teams while developing talent, allocating resources, and guiding scalable ModelOps and enterprise deployment.

Lead strategic sales of AI Workforce solutions, including Microsoft Copilot, AI agents, Viva, Windows 365, and Azure Virtual Desktop. Develop customer strategies, identify opportunities, build pipeline, conduct consultative business and technical conversations, and orchestrate internal teams and partners to close complex deals. The role requires expertise in technology sales, AI, forecasting, demand generation, stakeholder engagement, competitive positioning, and cross-selling across enterprise and regulated customers.

Lead analyst relations programs to improve Databricks’ positioning in industry evaluations, including Gartner, Forrester, and IDC. Develop differentiated narratives, submissions, demonstrations, customer evidence, and executive briefings. Scale analyst relations through AI-assisted workflows, convert analyst feedback into product and go-to-market recommendations, and influence product, marketing, sales, and executive teams. Manage multiple deadline-driven workstreams while improving analyst advocacy, market visibility, and evaluation outcomes.

The Director, AI Sales will grow Databricks’ enterprise AI business by driving strategic account adoption, production usage, bookings, and consumption. Responsibilities include developing AI sales plays, enabling field teams, securing executive and technical sponsorship, advancing complex opportunities, coordinating sales and partner stakeholders, and translating customer feedback into product and go-to-market recommendations.

Develop and support scalable Python services, APIs, and event-driven systems for digital freight pricing and booking. Build experimentation frameworks, AI-assisted pricing harnesses, validation guardrails, real-time platforms, and machine-learning integrations. Lead architecture decisions, ensure security and reliability, mentor engineers, and collaborate across product, operations, revenue management, data science, and engineering teams.

Own the commercial success of a portfolio of strategic enterprise accounts, including renewals, retention, expansion revenue, forecasting, and multi-year account strategies. Build executive relationships, lead business reviews and complex negotiations, coordinate cross-functional resolution efforts, and represent client needs internally. Mentor other Account Managers and use generative AI tools for account planning and communications while protecting confidential information.

Owns strategy, roadmaps, backlogs, and end-to-end delivery for business automation and AI products. Partners with engineering and executives to define requirements, launch reliable features, evaluate build-versus-buy decisions, manage budgets and cloud/API costs, measure ROI, and maintain product performance. Requires strong technical, agile delivery, analytics, and healthcare privacy expertise, with potential progression into product management leadership.

Designs, builds, evaluates, and deploys machine learning, LLM, and agentic AI capabilities for cybersecurity detection, investigation, and response. Analyzes complex security data, develops models and evaluation frameworks, creates RAG and retrieval workflows, and partners with engineering teams to productionize and monitor AI systems. The role requires addressing adversarial, noisy data and balancing accuracy, explainability, latency, reliability, and operational impact.