Top Machine Learning Jobs in Memphis, TN

Leads Dandelion Health’s AI developer business by building pipeline, closing clinical AI data and validation partnerships, recruiting developers into its marketplace, and achieving revenue targets. Develops commercial strategy, manages executive relationships, structures agreements, and collaborates with marketing, product, data, engineering, and clinical informatics teams. Represents the company at industry events and advises on clinical AI validation, regulatory, payer, and competitive dynamics.

Researches, develops, tests, and maintains materials-aware machine learning models and software. Designs scalable ML systems, improves model interpretability and uncertainty quantification, and develops inverse-design capabilities. Collaborates with product, engineering, and external research teams to deliver customer-focused AI solutions, publishes research, reviews code, analyzes model performance, and mentors developers.

Develop and deploy machine learning algorithms that improve real-time device identification and fraud detection. Own data science projects from experimentation through production, including supervised, semi-supervised, and unsupervised learning, real-time inference, training automation, exploratory analysis, and model-to-service integration. Contribute to engineering best practices, collaborate within a global remote team, and participate in a shared on-call rotation.

Improve 1Password’s technical SEO across consumer, PLG, and enterprise websites. Responsibilities include diagnosing and resolving crawlability, indexing, rendering, performance, structured data, internal linking, and international SEO issues; implementing CMS changes; partnering with engineering on requirements and release QA; leading migration workstreams; and building monitoring, dashboards, automation, and repeatable processes using search, analytics, crawl, log, and CMS data.

Own product strategy and delivery for agentic document management, policy and procedure workflows, and compliance gap assessments. Partner with customers, design, engineering, and go-to-market teams to build reliable, explainable AI experiences that accelerate audit readiness and security program expansion. Responsibilities include defining product requirements, evaluating AI outputs, improving quality, synthesizing customer feedback, prioritizing roadmaps, and shaping product culture.

Own the vision, strategy, roadmap, discovery, delivery, and continuous improvement of Rezilient’s member experience products. Partner with clinical operations, engineering, design, customer success, and sales to ship accessible, engaging healthcare experiences. Define requirements, prioritize backlogs, establish product metrics, ensure healthcare compliance, and help integrate AI into care delivery. The role requires experience scaling consumer health products, building AI or machine learning features, and operating effectively in a regulated startup environment.

Provides strategic legal counsel on clinical trials, commercial biopharma services, healthcare regulation, privacy, data protection, and EU employment matters. Advises on GDPR, HIPAA, clinical data use, cross-border transfers, privacy impact assessments, contracts, workforce initiatives, works councils, and regulatory developments. Partners with clinical, commercial, HR, compliance, technology, and senior leadership teams while supporting privacy program implementation and risk management.

Leads credit risk analytics for Prosper’s Credit Card business, overseeing underwriting, portfolio, pricing, credit line, fraud, and credit risk strategies. Uses machine learning, AI, and advanced analytics to manage growth while controlling losses within risk appetite. Presents recommendations to executives, the Credit Risk Committee, and investors; monitors performance and emerging trends; evaluates vendors and alternative data; and collaborates across Capital Markets, Product, Engineering, Legal, Compliance, Operations, and Marketing. Also builds and mentors high-performing credit risk teams.

Own and grow strategic Financial Services accounts through executive relationship management, consultative selling, renewals, and expansion. Lead commercial strategy, SOWs, negotiations, QBRs, delivery governance, escalations, and value realization. Coordinate delivery, sales, solutions, cloud, and Databricks partners while managing scope, budgets, timelines, margins, retention, profitability, and customer satisfaction. Translate Databricks, data engineering, analytics, ML, AI, and cloud transformation capabilities into measurable business outcomes.

Leads regional life science enterprise sales by developing pipeline, closing complex deals, expanding accounts, and building executive relationships across health plans, health systems, biotech, and pharmaceutical organizations. The role requires consultative selling of AI/ML healthcare solutions, cross-functional collaboration, CRM management, contract negotiation, and trusted-advisor engagement with C-level decision-makers. This remote position requires 25–50% travel.

Drive enterprise sales of Snowflake’s AI and machine learning solutions by generating pipeline, achieving revenue targets, developing territory strategies, and executing tailored sales plays. Engage executives and technical decision-makers, manage complex sales cycles, collaborate with account executives, sales engineers, field CTOs, and cross-functional teams, and provide customer insights to inform product strategy. Track sales performance and guide customers through AI/ML solution adoption, implementation, and deployment.

DoD SkillBridge Fellows gain practical experience through assigned projects, collaboration with experienced professionals, and exposure to operational processes, technologies, and mission objectives. Opportunities span telecommunications, acquisition and procurement, technology, accounting, finance, and other areas. Participants are compensated by their military employer during the fellowship and may be considered for full-time employment afterward based on performance and organizational needs.

Defines, prioritizes, and delivers data analytics and AI-enabled products supporting enterprise reporting and data-driven decisions. Acts as the liaison among business stakeholders, data engineering, analytics, platform, and data science teams. Establishes product direction, identifies AI and machine learning opportunities, ensures alignment with strategic objectives and data governance standards, and manages multiple initiatives and stakeholder groups.

Lead enterprise data security architecture to protect sensitive data across its lifecycle. Define strategies for encryption, key management, tokenization, masking, DLP, classification, database security, and Zero Trust. Advise execs and engineering on secure design patterns for data platforms (e.g., Starburst), cloud/on-prem systems, APIs, data pipelines, and AI/ML workloads. Develop roadmaps, evaluate technologies, mentor staff, and drive secure-by-design adoption across the organization.

Lead data science projects from ideation through enterprise impact, building scalable and well-tested solutions that improve healthcare outcomes. Apply probability, statistics, machine learning, SQL, and Python to develop data-driven products and insights. Partner with stakeholders, assess product performance, identify strategic improvements, mitigate risks, and mentor teammates while aligning initiatives with business objectives.

Own end-to-end strategy, roadmap, delivery, adoption, safety, and commercial readiness for AI-native cybersecurity products. Conduct customer and partner discovery, lead 0-to-1 launches, define experimentation and evaluation frameworks, and collaborate with engineering, design, sales, finance, and executives. The role requires hands-on LLM product expertise, strong judgment in ambiguous environments, and a focus on trusted AI experiences for security professionals and MSP/MSSP partners.

Develop and deploy AI solutions from problem framing through production. Responsibilities include identifying AI opportunities, leading MVP development, building data pipelines, experimenting with generative LLMs, fine-tuning NLP models, and delivering scalable features. The intern will collaborate cross-functionally, communicate technical impact to business audiences, and work with cloud platforms such as AWS or Google Cloud.

Support partner sales opportunities through discovery calls, product demonstrations, technical responses, solution scoping, project planning, proof-of-concept coordination, RFI/RFP responses, and security questionnaires. Collaborate with sales, professional services, product, engineering, and delivery teams while building partner relationships and developing expertise in fraud prevention and financial crime solutions.

Lead end-to-end product design for an AI-powered women’s health platform. Responsibilities include defining UX vision, creating interaction models, information architecture, wireframes, high-fidelity interfaces, prototypes, design systems, and user research. Collaborate with product, engineering, ML, marketing, and leadership teams to translate business needs into customer-focused solutions, manage design initiatives, and measure product success through user feedback and metrics.

Lead Nabla’s marketing organization and product marketing strategy across healthcare segments, buyer types, and direct and partner channels. Own positioning, messaging, launches, competitive intelligence, sales enablement, demand strategy, content prioritization, marketing operations, pipeline attribution, and team development. Manage a 6–8-person team spanning product marketing, growth content, design, and marketing operations while guiding resourcing and hiring decisions.

Leads the transformation of Sophos’s pricing operations by building a centralized function, establishing pricing governance and review cadences, developing AI-assisted competitive and discount analysis capabilities, and aligning pricing across SecOps and Network Security. The role provides executive market visibility, defines performance metrics, supports organizational change, and builds a high-performing team serving sales, finance, and customer-facing stakeholders.

The Tarbell Fellowship combines three months of remote training in AI and journalism fundamentals with a nine-month newsroom placement as an artificial intelligence reporter. Fellows write and investigate AI-related topics, build journalism skills, complete coursework and assignments, and attend a weeklong in-person summit. The program is full-time and intended for candidates with journalism or AI expertise, strong writing ability, persistence, curiosity, and open-mindedness. Senior fellowships are available to candidates with at least seven years of experience and exceptional prior work.

Lead the strategy, roadmap, launch, adoption, and ongoing improvement of an AI-powered clinical data insights platform. Partner with Data Science, Engineering, and application teams to create reusable capabilities, define platform metrics, prioritize investments, evaluate build-versus-buy decisions, and align senior stakeholders across clinical, technical, and business objectives.

Own the strategy, roadmap, and investment priorities for foundational agricultural data products, including pipelines, data models, APIs, and shared services. Partner with engineering, architecture, data science, and product teams to deliver scalable, governed, high-quality data capabilities. Prioritize initiatives based on customer and commercial value, define success metrics, resolve cross-team tradeoffs, and expand data support for analytics, predictive modeling, machine learning, and AI applications.

Lead end-to-end LLM inference performance across serving systems, models, GPU hardware, and workloads. Define rigorous benchmarks, diagnose bottlenecks from scheduling through kernels and interconnects, optimize single- and multi-node deployments, and deliver production-ready runtimes and configurations. Collaborate with product and infrastructure teams, evaluate inference technologies, and implement runtime fixes. The role requires strong Python engineering, production experience with serving engines such as vLLM or SGLang, GPU profiling, and knowledge of modern inference optimization techniques.