Machine Learning Jobs in Santa Clara, CA

Analyzes workforce and healthcare data to identify trends, build predictive and machine learning models, and deliver actionable recommendations. Uses Python, Alteryx, and Power BI for analytics, visualization, and reporting. The role supports workforce strategy, employee engagement, organizational effectiveness, talent management, and healthcare cost insights. Responsibilities also include client collaboration, relationship management, team guidance, and applying risk adjustment and medical economics concepts to inform strategic decisions.

Leverage advanced analytics, data manipulation, visualization, statistical modeling, predictive analytics, and machine learning to generate client insights and recommendations. Apply Adobe Experience Cloud solutions to support business transformation and strategic decision-making. Build client relationships, manage and inspire teams, navigate complex engagements, uphold professional standards, and deliver quality outcomes while developing technical and consulting expertise.

Leads data analysis and advanced analytics workstreams for clients, using statistical modeling, machine learning, data visualization, and Adobe Experience Cloud technologies to improve operations and strategic decision-making. Manages project planning, budgeting, execution, and completion while mentoring teams, maintaining data integrity and security, building Tableau and Power BI dashboards, identifying business opportunities, and integrating innovative analytics practices.

Leads internal audit and SOX engagements, manages client relationships, develops audit methodologies, evaluates financial statements and internal controls, conducts risk assessments, and oversees audit reporting. The role includes project ownership, budgeting, stakeholder management, team leadership, mentoring, process improvement, and use of AI and risk technology to enhance audit effectiveness and address compliance and governance risks.

Design, develop, and operate scalable VoIP infrastructure and telephony platforms supporting AI-driven voice workloads. Integrate SIP/RTP, Kamailio, RTPEngine, PSTN systems, LLMs, and real-time communication technologies. Build reliable cloud-native distributed systems on Kubernetes, improve SRE and observability practices, automate deployments, develop software tooling, and maintain high-quality code. Collaborate across engineering, product, infrastructure, and AI teams while mentoring colleagues.

Leads architecture, optimization, deployment, and operational excellence for an AI inference platform. Owns model onboarding, optimized production inference across multiple backends, Kubernetes operations, gateway architecture with Envoy and ext_proc, observability, incident response, performance tuning, and secure AI-assisted development. Requires extensive backend engineering and distributed systems experience, production Kubernetes expertise, and strong Go or Python skills.

Leads AI/ML infrastructure networking initiatives by benchmarking switches, validating ASIC and fabric features, and guiding workload deployments. Develops AI-driven network automation using Python, Ansible, SONiC, Junos, and related tools. Tests protocols, congestion behavior, traffic distribution, resiliency, and high-performance network operations. Documents architectures, methodologies, configurations, results, and recommendations for scalable AI/ML network fabrics.

Design, train, fine-tune, optimize, and deploy large-scale machine learning systems for cloud security use cases. Build end-to-end ML pipelines, develop transformer and embedding models, productionize open-weight language models, and optimize inference for latency, cost, and quality. Architect resilient ML services across AWS and GCP using cloud-native microservices while collaborating with engineering teams on AI strategy and solving complex problems involving massive datasets.

Build and operate cloud-based AI and machine learning solutions for enterprise services. Develop efficient, modular software in Go or Python; collaborate with product, development, and quality teams; and own solutions through design, testing, deployment, and delivery. Deploy and scale services in Kubernetes using Helm and CI/CD, maintain observability, resolve production incidents, participate in on-call rotations, and perform root-cause analysis against service-level objectives.

Owns end-to-end evaluation and validation processes for autonomous vehicle software and end-to-end machine learning stacks. Designs scalable metrics, ELT infrastructure, automated triage strategies, and KPI monitoring using simulation and on-road data. Performs root-cause and behavioral analysis, communicates data-driven recommendations, and connects continuous improvement efforts to product safety cases and compliance standards. Collaborates with AI/ML, simulation, systems engineering, and product teams.

Leads acquisition and governance of clinical and EEG datasets supporting new indications and algorithm development. Builds academic and hospital partnerships, evaluates research proposals, negotiates data access, oversees EMR integrations, assesses dataset quality, and coordinates with Data Science, Clinical, Legal, Regulatory, and Privacy teams on research collaborations, data agreements, IRB requirements, de-identification, and HIPAA compliance.

Serves as a technical lead for customers using CoreWeave cloud infrastructure, with emphasis on Kubernetes in high-performance computing environments. Responsibilities include designing and deploying tailored solutions, leading proofs of concept, optimizing AI/ML workloads, conducting technical reviews, advising on product strategy, collaborating with engineering teams, and representing CoreWeave at industry events.

Evaluate and implement AI tools, agent harnesses, and models for software engineering workflows. Build evaluation pipelines and reference workflows, optimize distributed systems for performance and cost, collaborate with vendors, guide tool selection, and train engineering teams. The role requires strong Python software engineering, LLM and agentic AI expertise, CI/CD knowledge, distributed systems experience, and the ability to translate evaluations into practical recommendations.

Own the end-to-end roadmap and lifecycle for Cisco’s enterprise AI agentic platform and collaboration experiences. Develop product strategy, gather requirements, author PRDs, and align engineering, design, security, legal, and business stakeholders. Lead focus groups, user testing, prototypes, integrations, and platform architecture decisions. Define success metrics, analyze adoption data, and deliver secure, privacy-forward AI solutions across enterprise tools and workflows.

Leads architecture and hands-on development of scalable governance, risk, compliance, and privacy platforms. Designs microservices and REST APIs using Java, Spring Boot, Node.js, and cloud technologies; drives platform strategy, CI/CD, reliability, and performance. Leads medium-to-large engineering projects, mentors engineers, conducts design and code reviews, and collaborates with product, business, UX, and program teams. Explores Agentic AI and machine learning solutions while supporting agile development and automated testing.

Leads the design and delivery of scalable cloud-native platform capabilities for Walmart Marketplace. Responsibilities include software development lifecycle ownership, coding, testing, deployment, monitoring, system design reviews, AI/ML application development, automation, technical mentoring, stakeholder collaboration, and continuous improvement of engineering processes and products.

Leads product strategy and execution for Walmart’s vertical search and discovery experiences. Owns vision, roadmap, relevance, ranking, personalization, trust, and AI-driven shopping capabilities. Partners with Engineering, Design, Data Science, and business stakeholders to deliver scalable solutions, run experiments, analyze customer and query data, and improve discovery, conversion, and category growth. The role requires influencing senior stakeholders and leading complex initiatives in a highly matrixed organization.

Owns how Cisco products integrate with Cloud Control by defining integration tiers, requirements, dependencies, roadmaps, and launch plans. The role builds self-service integration capabilities including APIs, contracts, sandboxes, validation tools, and onboarding checklists. It partners with engineering, design, identity, security, legal, privacy, support, and go-to-market teams; tracks adoption, defects, integration time, and launch performance; and drives consistent cloud product experiences across Cisco business units.

Research and prototype wireless sensing systems for augmented reality, wearable, and robotic applications. Design hardware platforms and software infrastructure, develop wireless perception and sensing algorithm prototypes, validate technologies, collaborate with product and research teams, communicate technical outcomes, and publish findings. The role requires experience with RF systems, signal processing, machine learning, and relevant programming and ML frameworks.

Develop and automate failure-analysis analytics, dashboards, and reporting using machine learning, LLMs, AI, SQL, and Python. Define project requirements, migrate visualizations from Spotfire to Apache Superset and Power BI, automate electrical and mechanical failure-analysis workflows, and improve reporting turnaround times. Collaborate with laboratory and cross-functional teams, communicate project status, and serve as the team’s technical expert for analytics, AI, SQL, and dashboard solutions.

Designs and implements evaluation, validation, testing, and simulation frameworks for autonomous vehicle software and ML systems. The role analyzes simulation and on-road data, develops performance metrics and confidence measures, performs root-cause analysis, automates regression detection, and communicates evidence-based insights to technical stakeholders. It requires strong Python skills, systems-engineering experience, analytical problem solving, and cross-functional collaboration.

Leads technical program management across product engineering, platform, delivery, and operations. Owns cross-team planning, sequencing, dependency and risk management, engineering delivery cadence, launch readiness, and AI program execution. Manages scrum masters and technical program managers, improves engineering flow using delivery metrics, coordinates internal and external product launches, and provides technical judgment on architecture, quality, cost, and schedule risks.

Drive strategy and execution for Cisco’s agentic Security Operations Center platform. Partner with engineering and customers to identify security operations challenges, define product requirements, prototype solutions, and validate results. Collaborate with go-to-market teams to launch capabilities and lead analyst briefings. The role requires expertise in SOC workflows, SIEM, SOAR, XDR, threat intelligence, incident response, and AI or machine learning technologies.

Develop and optimize ASIC/FPGA timing, power, library generation, and signoff flows. Own STA, UPF-based power methodologies, library characterization, reporting, automation, and flow debugging. Apply ML/AI, data analytics, and anomaly detection to improve QoR, convergence, productivity, and predictive analysis. Collaborate with physical design, CAD, library, power, and reliability teams on advanced semiconductor designs.

Leads the architecture, development, and deployment of physics-based modeling and simulation algorithms for e-beam semiconductor inspection systems. Develops SEM image formation, electron-beam interaction, and voltage-contrast technologies; integrates physics-based methods with computer vision and AI; translates physical phenomena into production software; influences technical roadmaps; and mentors junior engineers. Requires a PhD, 8+ years of experience, strong C++/CUDA/Python skills, and semiconductor experience preferred.