Top Remote Machine Learning Jobs in Memphis, TN

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.

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.

Define and scale autonomous agricultural machinery products through customer research, experimentation, roadmap prioritization, system requirements, and cross-functional delivery. Partner with engineering and commercial teams on hardware, embedded software, computer vision, machine learning, navigation, controls, and mobile solutions. Use data, field feedback, and performance metrics to improve adoption, customer confidence, mission quality, and product impact.

Own products supporting post-close loan operations, including servicing, payments, money movement, valuations, and vendor integrations. Build roadmaps, prioritize backlogs, coordinate engineering and operational stakeholders, troubleshoot production issues, and improve processes through automation and AI. Partner with Treasury, Servicing, Capital Markets, Compliance, and Operations while defining metrics, measuring ROI, and delivering products across their lifecycles.

Conduct empirical AI safety research with mentor guidance, owning projects from hypotheses and controlled experiments through analysis and technical writing. Build and debug evaluation harnesses, agent environments, datasets, monitoring methods, and post-training experiments. Investigate model failures, alignment, deception, misuse, multi-agent risks, and evaluation reliability. Produce reproducible code, experiment logs, research analyses, and papers for potential submission to leading machine learning conferences.

Own enterprise sales across new and existing accounts, leading consultative, ROI-driven sales cycles from discovery through close. Develop account plans, build executive relationships, collaborate with Solutions and Forward Deployed Engineers, forecast pipeline, maintain CRM discipline, lead business reviews, represent the company at industry events, and relay customer insights to shape the product roadmap.

Develop and support software, automation tools, dashboards, and alarms for cloud infrastructure, cost attribution, and cloud efficiency. Collaborate with cross-functional teams and senior engineers to design reliable systems, APIs, databases, tests, and AI agents. Contribute to architecture and automation strategies while maintaining strong security, communication, and delivery practices.

Own strategic ISP customer accounts across onboarding, adoption, value realization, retention, renewals, and expansion. Serve as an executive trusted advisor, lead business reviews, mitigate churn, and partner with Sales, Product, Marketing, and Engineering. Help scale Plume’s Customer Success function through improved playbooks, tooling, engagement models, customer advocacy, and standardized methodologies.

Leads AF Group’s AI and emerging technology strategy, translating business priorities into prioritized use cases, business cases, roadmaps, and measurable outcomes. Directs cross-functional delivery from experimentation through production adoption, coordinating security, platform, application, data, and Center of Excellence partners. Evaluates generative AI, machine learning, and automation technologies; manages budgets, resources, change management, operational readiness, adoption, and value realization. Supervises employees and develops organizational AI capabilities.

Develop graduate-level Applied AI course materials, including lessons, labs, case studies, assignments, rubrics, projects, readings, templates, and videos. Design practical activities using LLMs, RAG, AI agents, automation, APIs, and evaluation tools. Collaborate with learning designers and university faculty, explain complex concepts to nontechnical learners, maintain academic quality, and update content as AI tools and regulations evolve.

Leads the design, implementation, and evolution of enterprise security capabilities across federal cloud and hybrid environments. The role engineers Zero Trust architectures, FedRAMP High controls, identity and access management, encryption, logging, vulnerability management, monitoring, and incident response. Responsibilities include supporting ATO authorization, continuous monitoring, risk remediation, security assessments, and secure adoption of AI-enabled technologies. The position requires collaboration with cloud, infrastructure, DevSecOps, compliance, and operations teams.

Drives business transformation and strategic change initiatives by developing transformation strategies, improving processes and organizational design, managing change adoption, engaging senior stakeholders, coordinating cross-functional projects, delivering training, and tracking benefits realization. The role manages transformation projects from planning through post-implementation review while ensuring alignment with business objectives, timely delivery, budget adherence, and successful organizational adoption.