Top Remote Machine Learning Jobs in Raleigh, NC

Own and scale Jellyfish’s security engineering program, including strategy, architecture, secure SDLC governance, AI security, threat modeling, vulnerability remediation, incident response, penetration testing, and bug bounty programs. The role combines hands-on software development with executive advising, cross-functional leadership, mentorship, and security risk management across SaaS infrastructure and customer-facing AI products.

Drive product and business impact through statistical modeling, experiment design, Bayesian marketing analysis, machine learning, NLP, LLM applications, and classifier implementation. Build growth metrics, monitoring pipelines, evaluation datasets, and AI trigger systems across search and Duck.ai. Analyze privacy-constrained data, deploy production-grade models, optimize SQL queries, and lead complex projects from scoping through production with minimal direction.

Own strategy and roadmap for Crunchbase's customer-facing AI data layer. Identify and validate model-derived data products, lead cross-functional development, set quality standards, drive adoption and monetization, and monitor performance and product economics.

Build and own production-grade generative AI applications for legal technology, specializing in RAG, agentic systems, advanced search, embeddings, reranking, evaluation, and LLM workflows. Responsibilities include architecting modular Python applications, refactoring legacy code, implementing testing and observability, optimizing performance and costs, ensuring resilience and security, reviewing code, and supporting deployments and incident resolution.

Own the strategy, roadmap, requirements, launch, adoption, and success metrics for a technical platform. Partner with engineering, data, product, and business teams; evaluate architecture, APIs, and system tradeoffs; prioritize foundational investments against customer and revenue outcomes; and apply AI to improve platform capabilities. The role requires strong product management experience, technical depth, analytical skills, and production experience with LLM-based or ML-adjacent products.

Drives HPE private cloud AI sales across a multi-state territory, creating pipeline, developing net-new business, engaging executives, and closing complex enterprise opportunities. Advises customers on generative AI, infrastructure, hybrid cloud architectures, economics, security, and governance. Coordinates account managers, architects, services, partners, and executives through discovery, validation, business-case development, negotiation, and close while serving as an AI sales subject-matter expert.

Lead AMP's multidisciplinary engineering organization to define and execute technology vision and roadmap. Responsible for scaling teams across hardware, software, AI, and infrastructure; driving product development, quality systems, and reliable deployments; partnering with execs and cross-functional teams on budgets, prioritization, and metrics; recruiting and mentoring leaders; and balancing short-term delivery with long-term investments to create business value.

Advise Product, Engineering, Security, and business teams on software, SaaS, cloud, privacy, cybersecurity, AI, intellectual property, and emerging technology regulations. Guide products throughout their lifecycle, assess legal and regulatory risks, draft agreements and privacy documentation, support privacy-by-design, monitor global regulations, and develop scalable legal processes, playbooks, policies, and training.

Develop and deploy statistical and machine learning models for credit risk, delinquency, defaults, loan losses, underwriting, and risk-based pricing. Analyze credit bureau, alternative, and loan performance data; build production-ready analytical pipelines; monitor model performance; and address data anomalies. Lead complex initiatives, mentor colleagues, document methodologies, and incorporate model governance, fair lending, privacy, and responsible AI considerations while partnering with Credit Risk, Product, Engineering, and Finance.

Drive AI-enabled product strategy and execution by identifying customer opportunities, defining requirements, and partnering with engineering, design, data, sales, marketing, and customers. Lead development of products using LLMs, generative AI, RAG, and intelligent agents; validate roadmap priorities, communicate product vision, support launches, and improve feature adoption. Apply AI tools to accelerate product specifications, workflows, and software delivery.

Lead the design and development of Azure Compute Fleet capabilities that orchestrate Virtual Machine Scale Sets across regions. Build distributed control-plane services, APIs, and workflows for capacity placement, scaling, failover, and lifecycle management. Collaborate across Azure engineering teams, drive AI-enabled engineering improvements, participate in on-call operations, and improve reliability, observability, performance, and recovery for hyperscale cloud services.

Design, build, and deploy production AI automation systems for logistics workflows. Develop agentic LLM systems, RAG pipelines, document-processing solutions, monitoring, guardrails, validation, and fallback mechanisms. Identify high-value automation opportunities and collaborate with operations, brokerage, and customer success teams to improve freight processes such as carrier onboarding, tendering, invoice reconciliation, exception handling, and customer communications.

Leads revenue, go-to-market execution, forecasting, and organizational growth for strategic enterprise accounts. Builds and manages sales leadership and enterprise account teams, drives AI-focused value selling, oversees complex negotiations, and develops Fortune 500 and Global 2000 relationships. The role partners cross-functionally on customer expansion, product strategy, marketing, customer success, hiring, budgeting, and operational planning. Requires extensive enterprise SaaS sales leadership, experience with developer tools or cloud infrastructure, AI solution expertise, and executive-level engagement.

Implements and maintains cloud security controls across Microsoft 365 GCC High, Azure, AWS, and hybrid environments. Administers cloud security platforms, integrates telemetry, supports vulnerability remediation, and validates controls against CMMC, NIST, DFARS, ITAR, ISO 27001, and other requirements. Secures generative AI, machine-learning, copilot, RAG, and agentic systems through access controls, sandboxing, logging, and execution safeguards. Performs risk assessments, control testing, audit evidence collection, SOC escalation support, automation, documentation, and occasional off-hours incident support.

Designs, implements, and maintains cloud security controls across Microsoft 365, GCC High, Azure, AWS, and hybrid environments. Leads cloud and AI security architecture, threat modeling, infrastructure-as-code automation, security testing, posture management, and incident support. Develops secure patterns for generative AI, machine learning, copilots, agents, and CUI workloads. Translates CMMC, NIST, DFARS, ITAR, ISO 27001, GDPR, and related requirements into technical controls, evidence, and risk mitigations while mentoring engineers and guiding cross-functional teams.

Leads enterprise healthcare interoperability strategy, operations, connectivity, and strategic partnerships. Establishes operating models, roadmaps, KPIs, governance, and partner ecosystems across EHRs, HIEs, QHINs, payers, providers, and data networks. Translates regulatory and technical requirements into scalable capabilities, supports product and commercial growth, oversees investments and agreements, and builds high-performing teams. Requires executive leadership in healthcare data exchange, interoperability standards, regulatory strategy, operations, partnerships, and technology commercialization.

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.

Drive data-informed marketing decisions through business analysis, machine learning, experimentation, causal inference, and statistical testing. Develop measurement frameworks, evaluate campaign effectiveness, identify growth opportunities, and create targeting, predictive modeling, and personalization solutions. Collaborate with product, marketing, sales, growth, and leadership teams to translate business challenges into analytical solutions and present actionable recommendations. The role is remote, six months, and requires 6+ years of relevant experience.

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.

Own the AI product strategy, backlog, roadmap, discovery, requirements, and delivery for Quinn, a platform supporting SEC disclosure workflows. Partner with customers, engineering, legal, security, and sales to define trusted AI capabilities, evaluation frameworks, quality standards, governance practices, pilot programs, and release priorities. Translate customer and market insights into validated solutions while balancing technical feasibility, regulatory requirements, security, privacy, and business objectives.

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.