Top Machine Learning Jobs in Greensboro, NC

Build and operate secure, scalable production infrastructure for agentic AI platforms across AWS and Azure. Responsibilities include CI/CD automation, infrastructure as code, cloud security, governance, monitoring, observability, model lifecycle management, drift detection, incident response, networking, resilience, and cost optimization. The role establishes production-readiness standards, supports AI deployments, and partners with engineering, product, security, compliance, and business teams.

Leads AI and computational algorithm research, development, clinical validation, deployment, and lifecycle monitoring for oncology diagnostics. Sets foundation model and multimodal AI strategy, prioritizes clinical use cases with Product and clinical stakeholders, manages multidisciplinary algorithm teams, recruits technical leaders, and ensures regulatory-quality data, validation, reproducibility, release controls, and post-launch performance monitoring.

Own and build Fireworks’ customer marketing function, including advocacy, enterprise reference programs, customer stories, peer-to-peer events, co-marketing, awards, and customer-led expansion initiatives. Recruit and develop technical and executive advocates, create compelling proof for sales and marketing, manage customer approvals, and collaborate with Sales, Customer Success, Product Marketing, Developer Relations, Product, and Events. The role requires hands-on strategy and execution in a fast-moving AI infrastructure company.

Research and prototype AI-powered tools for music generation, transformation, remixing, and audio understanding. Build and evaluate machine learning models using Python and modern frameworks, process audio datasets, assess model performance and musical quality, and collaborate with engineering, music technology, product, and creative teams. The intern will also document findings and contribute to responsible AI practices involving artist consent, music rights, and ethical data use.

Develops statistical, machine learning, AI, and predictive solutions to solve complex business problems. Mines and analyzes large datasets, builds algorithms and automated data processes, validates models, and communicates insights to business and product leaders. Designs enterprise AI applications, including generative AI and RAG solutions, while supporting data governance, visualization, security, scalability, monitoring, and responsible AI practices.

Lead the technical aspects of enterprise security sales, including discovery, tailored demonstrations, proof-of-value engagements, RFP responses, and architecture discussions. Build relationships with SOC analysts, security architects, and CISOs; translate customer feedback into product improvements; create reusable sales enablement assets; and support early implementation handoffs. The role requires security operations expertise, cloud knowledge, scripting ability, and familiarity with AI and machine learning concepts.

Build and deploy machine learning models for credit and fraud risk, using large-scale financial data to improve lending decisions and business performance. Monitor production models, validate algorithms and features, conduct risk analyses, follow regulatory and responsible AI practices, and collaborate with engineers and cross-functional stakeholders. Develop productivity tools, document models, and communicate technical findings to senior management.

Own the strategy, vision, roadmap, and execution for Proof’s Defend anti-fraud product. Lead fraud investigation and risk prediction tools, customer discovery, strategic planning, GTM, stakeholder alignment, and executive communications. Collaborate with engineering, data science, design, fraud operations, sales, and customer success to define technical requirements, prioritize development, improve onboarding, respond to fraud cases, and deliver data-driven identity and transaction protection solutions.

Build product enablement for global go-to-market teams across AI Agents, APIs, and Creative products. Responsibilities include preparing launch workshops, competitive training, product certifications, onboarding and everboarding content, hands-on demos, role-play simulations, practice environments, and performance measurement. The role partners with product marketing, engineering, research, and enablement teams while using ElevenLabs’ AI technology to create training experiences.

Build and operate a scalable LLM inference platform integrated with the MAX stack. Develop distributed, multi-node model deployments, high-throughput batching, KV-cache management, autoscaling, observability, cold-start optimizations, and multi-cloud capabilities. Create Helm charts and Kubernetes operators while collaborating with kernel and generative AI teams to optimize performance. The role requires backend engineering, Kubernetes operations, machine learning experience, and distributed systems expertise.

Own the offline identity resolution product roadmap, prioritize and manage phased releases, write requirements, maintain the backlog, and coordinate launches with Engineering, Data Science, Client Success, and Sales. Lead matching model, data pipeline, schema, and Agentic UI enhancements; define release criteria and success metrics; address client pain points; and support data connectivity between Bridg’s Snowflake warehouse and PAR Retail’s data lake.

Analyzes large-scale telemetry and datasets to identify content delivery performance and reliability issues. Builds software tools to automate analytical workflows and enable autonomous diagnosis. Applies statistical analysis, machine learning, networking, and distributed-systems expertise to develop targeted platform improvements. Defines and mines internal data feeds, establishes performance indicators, and partners with Engineering, Operations, and Product teams to guide feature and process adoption.

Build and improve machine learning models that identify healthcare claims errors and fraud for audit. Responsibilities include curating labeled data, engineering features, selecting optimization metrics, measuring model and business impact, refining large-scale data pipelines, and developing modeling infrastructure and team tools. The role also involves collaborating with clients and healthcare experts to align insights with business needs.

Supports clinical operations through data entry, validation, reconciliation, reporting, system maintenance, and data quality checks. Collects and transforms datasets, develops visualizations and analytical models, supports patient recruitment tracking, troubleshoots routine system issues, and maintains documentation. Collaborates with Data Management, Clinical Operations, and Patient Recruitment teams while protecting confidential information.

Own the end-to-end mobile identity verification product across SDKs, web flows, and APIs. Lead roadmap, UX, biometric intelligence, deepfake detection, liveness technology, conversion optimization, fraud prevention, and global scalability. Partner with engineering, research, design, customer success, and commercial teams to deliver scalable offerings, support customer adoption, identify upsell opportunities, and use analytics and experimentation to improve performance.

Develops data science, machine learning, and generative AI solutions for customer engagements and pre-sales opportunities. Translates ambiguous business challenges into analytical use cases, prototypes, hypotheses, demonstrations, and actionable recommendations. Explores customer data, applies statistical and advanced analytical techniques, communicates findings to technical and business audiences, and creates reusable analytics assets. Partners across Microsoft teams to connect customer data and AI opportunities with broader transformation priorities while applying responsible AI, security, governance, and data quality principles.

Conduct machine learning and data science research for drug discovery, focusing on mass spectrometry, computational biology, and computational chemistry. Own projects from scientific question through results, develop data-driven solutions, validate AI-generated analyses, and communicate findings through visualizations and writing. Collaborate with the Data Science team and contribute to applications of AI in medicine development.

Build and deploy production machine-learning models for member identification, personalization, provider search, and healthcare AI tools. Lead projects from problem framing and prototyping through product handoff, design experimentation and causal analysis, define product metrics, and develop reliable feature pipelines and experiment infrastructure. Partner with product, engineering, care operations, marketing, and analytics engineering while communicating recommendations clearly and ensuring models are monitored, tested, and impactful.

Senior Product Manager responsible for the vision, strategy, and roadmap of Root’s pricing platforms and rating engine. The role leads cross-functional prioritization across engineering, data science, analytics, actuarial, risk, legal, compliance, and state management. Responsibilities include accelerating model deployment, improving data services, building experimentation tools, governing APIs and event-driven architectures, reducing regulatory bottlenecks, and supporting expansion into new states and markets.

Develop and integrate production AI/ML capabilities into Oracle E-Business Suite on OCI for federal financial management. Build NLP and document-processing solutions, REST API integrations, and end-to-end ML pipelines. Define AI/ML use cases, transition roadmaps, cognitive adoption metrics, and data-mining strategies. Participate in SAFe Agile delivery, write user stories, remove technical impediments, support DevSecOps integration, and produce transition and knowledge-transfer materials.

Lead and build a new platform engineering team responsible for cloud-agnostic deployments across AWS, other public clouds, private cloud, and on-premises environments. Own Kubernetes, infrastructure as code, GitOps, CI/CD, security compliance, FinOps, observability, incident response, and AI/ML infrastructure. Hire and develop engineers, establish operating standards, automate compliance evidence, and deploy AI agents for infrastructure operations.

Build and manage technical relationships with enterprise customers, helping them adopt JetBrains products, overcome technical and organizational barriers, and achieve business outcomes. Serve as a customer advocate, advise on software development tooling, collaborate with account executives and product teams, recommend product enhancements, support account expansion, and present product capabilities. The role requires customer-facing technical experience, AI/LLM knowledge, strong communication, software development team experience, and approximately 25% travel.

Lead technical strategy and architecture for Azure Storage geo-replication, data protection, failover orchestration, and business continuity. Drive cross-team delivery, operational excellence, telemetry, automation, AI-assisted operations, and represent the team in executive and customer engagements.

Develop and productionize large-scale machine learning models, ranking algorithms, data pipelines, and real-time or batch systems for Airbnb search and recommendation. Collaborate with engineering, product, operations, and data science teams to prioritize opportunities, drive technical decisions, and measure business impact. Build scalable ML infrastructure supporting feature engineering, model serving, experimentation, interpretability, optimization, and concept drift detection.

Own cross-functional technical programs supporting Customer Success, coordinating Product, Engineering, NOC, and Operations. Validate customer-focused releases, configurations, and integrations; lead change management, enablement, communications, and training; triage customer-impacting technical issues; maintain PMO trackers, status reports, and risk logs; and advise leadership on priorities and customer needs.