Top Machine Learning Jobs in Raleigh, NC

Leads technical customer engagements for strategic enterprise accounts, combining solution engineering, software implementation, consulting, and sales support. Responsibilities include discovering requirements, designing and documenting SailPoint solutions, conducting foundational implementations and proof-of-value engagements, educating customers on architecture and best practices, and developing AI-enabled demonstrations and agentic solutions. The role collaborates across sales, product, professional services, partners, and customer success, and may require up to 15% travel.

Build and operate production machine learning systems for Safety AI, including low-latency APIs, data pipelines, model serving, evaluation, monitoring, and rollout infrastructure. Process large-scale camera and telematics data, optimize cloud and edge-to-cloud execution, track model performance and drift, and partner with applied scientists, firmware engineers, platform teams, and product managers to deliver reliable safety features.

Engineer AI-powered threat detections, automated response playbooks, and continuous adversary emulation. Apply machine learning and LLMs to security operations, conduct threat hunting and AI red teaming, map coverage to MITRE ATT&CK, and develop detection-as-code through CI/CD. Build production-grade Python integrations, measurable containment capabilities, and safe autonomous defenses with guardrails. Mentor security professionals and collaborate across threat response, cyber defense engineering, and platform teams.

Design, build, and operate high-throughput, low-latency ad-serving infrastructure processing millions of requests per second. Optimize GPU-powered TensorRT inference, feature stores, model serving, benchmarking, experimentation tooling, and system reliability. Partner with machine learning engineers to productionize models and own systems through design, deployment, observability, capacity planning, incident response, and optimization.

Leads the strategic direction of independent medical education and scientific content solutions. Provides clinical oversight, develops evidence-based content strategies and therapeutic plans, ensures medical accuracy and defensibility, and partners with editorial, product, project, commercial, and scientific stakeholders. The role monitors clinical evidence and regulatory developments, advises on complex content decisions, engages key opinion leaders and customers, and translates complex medical and scientific information into accessible educational products.

Leads semiconductor product engineering from concept through qualification and high-volume manufacturing. Drives characterization, statistical analysis, yield improvement, reliability and ESD qualification, failure analysis, root-cause investigations, and product release decisions. Applies AI and machine learning to engineering data workflows while influencing global cross-functional teams and mentoring engineers.

Leads Cisco’s senior product marketing team and global go-to-market strategy for secure networking and AI solutions. Partners with product, engineering, sales, and executive leaders to define positioning, messaging, market narratives, field enablement, and major market initiatives. Drives customer outcomes, pipeline, bookings, adoption, competitive performance, and market creation while managing managers and building a high-performing global team.

Leads the design and delivery of bespoke Generative AI and machine learning solutions for pharmaceutical and life sciences clients. Responsibilities include developing proofs of concept, guiding production deployment, leading multidisciplinary teams, mentoring staff, managing client engagements, presenting technical solutions to nontechnical stakeholders, and applying consulting principles to solve complex business challenges.

Leads software engineering teams developing the Health100 consumer health platform. Responsibilities include architectural leadership for distributed systems, APIs, and AI-enabled capabilities; recruiting and mentoring engineers; managing initiatives through delivery and production operations; establishing performance, risk, and governance practices; and partnering across product, design, security, analytics, and business teams. The role emphasizes Java, cloud-native development, microservices, Generative AI, intelligent automation, and scalable platform solutions.

Leads managed services onboarding, client experience, support model optimization, KPI development, business reviews, value communication, and churn-risk initiatives. Oversees IT support tools, SOPs, SLAs, runbooks, root-cause analysis, and AI-enabled service improvements. Builds client relationships, supports business development, mentors junior staff, manages concurrent projects, and maintains 1,700 annual billable hours.

Verify ASIC inference accelerator designs at block and SoC levels using advanced methodologies, random stimulus, functional coverage, assertions, and UVM. Develop verification infrastructure, debug designs, optimize automated flows, and collaborate with architects, designers, and pre- and post-silicon teams. Apply knowledge of ASIC design flow, power verification, DFT, netlist verification, and machine learning techniques to improve verification processes.

Build and productionize traditional machine learning models, owning feature engineering, validation, deployment, monitoring, drift detection, and retraining. Develop LLM-powered agentic tools, dashboards, self-serve analytics products, and automated insight pipelines. Partner with engineering and business stakeholders, write production-grade Python and SQL, drive adoption, improve data quality, and establish responsible AI workflows for a regulated industry.

Develop fraud and abuse detection models using large transaction, account, gameplay, and behavioral datasets. Responsibilities include data exploration, statistical and machine learning modeling, feature development with operations analysts, dashboard and monitoring creation, impact measurement, documentation, and presenting findings.

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.

Design and build a scalable failure-attribution framework for heterogeneous CPU/GPU clusters: collect high-fidelity telemetry, implement low-overhead tracing, automate root-cause classification (hardware, software, environment), and enable proactive resiliency measures like migration or checkpointing.

Design, build, and support high-throughput, low-latency LLM inference and data pipelines; collaborate with data scientists to post-train and evaluate models; implement scalable, production-ready systems with strong testing, monitoring, and performance focus; mentor engineers and iterate on architecture, reliability, and user-facing AI applications.

Own and improve Headway’s privacy program across HIPAA, 42 CFR Part 2, state privacy laws, individual rights, incidents, vendor governance, and AI use. Maintain controls, data maps, metrics, training, and operational workflows. Conduct product, vendor, and AI privacy assessments; manage rights requests and incident investigations; coordinate notifications and regulatory submissions; and translate complex requirements into practical guidance for cross-functional teams.

Own the strategy and roadmap for patient-provider search, retrieval, ranking, and marketplace matching. Lead experimentation, objective functions, evaluation, and ML platform foundations while partnering with ML engineers, data scientists, design, clinical teams, and executives. Improve successful, lasting care matches rather than optimizing only clicks or bookings. Balance patient access, provider growth, clinical quality, and payer priorities, while setting the standard for technically grounded product management.

Develop autonomy evaluation metrics, statistical and machine-learning analyses, dashboards, and pipelines for simulation and on-road autonomous vehicle testing. Analyze perception, prediction, and planning performance; identify anomalous behavior and critical scenarios; apply VLMs and LLMs where appropriate; and support release gating and safety decisions. The role requires production Python, C++ debugging, technical leadership, cross-functional collaboration, and rigorous software engineering practices.

Define architecture and metrics for autonomous driving evaluation, lead cross-functional integration across autonomy and simulation teams, develop statistical and machine learning methods, analyze large-scale time-series data, manage evaluation KPIs and release gates, and communicate safety and readiness insights through dashboards and stakeholder presentations.

Serves as the technical bridge between customers, Sales, and technology teams. Leads technical discovery, solution architecture, demonstrations, workshops, proofs of concept, and evaluations using HERE data, APIs, mapping, routing, spatial analytics, cloud, AI, and enterprise integration. Partners with Sales to qualify opportunities, reduce technical risk, develop proposals and reusable presales assets, and guide customers toward location-intelligence solutions.

Analyze malware, binary files, customer detection tickets, and machine learning detections to improve detection efficacy and reduce false positives. Review product detections, assess binary legitimacy, address internal detection questions, and provide insights to data science teams. The role collaborates with technical account managers, incident response teams, data scientists, and malware researchers. Required capabilities include malware analysis, reverse engineering, Python scripting, binary fundamentals, threat assessment, research methods, operating systems, and AI-assisted workflow improvement.

Advance enterprise AI governance and risk management by conducting AI risk and control assessments, evaluating governance frameworks, analyzing AI architectures and data flows, monitoring risk metrics, and supporting assurance, audits, and regulatory examinations. The role partners with technology, cybersecurity, compliance, legal, audit, and business teams to promote secure, compliant, and responsible adoption of generative AI, machine learning, intelligent agents, and third-party AI solutions.

Leads the enterprise Workday platform and HR technology strategy, roadmap, governance, operational support, integrations, reporting, security, and continuous improvement. Oversees service delivery, release readiness, audit and SOX compliance, vendors, cross-functional programs, and platform-related risk. Partners with HR, Facilities, Finance, IT, Security, Legal, and Audit while leading a multidisciplinary team and evaluating AI opportunities. The role requires executive stakeholder management, strong Workday expertise, product leadership, and experience in complex regulated environments.

Analyzes complex datasets to identify trends, develops predictive models, and delivers data-driven insights for business decisions. Collaborates with stakeholders to identify growth opportunities, creates data visualizations, validates data quality, and stays current with emerging technologies.