Machine Learning Jobs in San Jose, CA

Develop AI-enabled software solutions for internal digital transformation initiatives. Design, code, test, debug, and support scalable applications using modern engineering practices. Apply machine learning tools, automate workflows, analyze data, contribute to APIs, microservices, and cloud-native architecture, and translate business requirements into technical designs. Review junior team members’ work, provide technical guidance, and collaborate with internal stakeholders to deliver reliable enterprise applications.

Leads the design, development, and deployment of scalable AI, generative AI, and machine learning solutions. Oversees data infrastructure, integration, data quality, and advanced analytics while managing client engagements and executive relationships. Drives business development, strategic direction, innovation, and AI-driven automation. Mentors teams, develops future leaders, and fosters collaboration, integrity, and client satisfaction across complex projects.

Leads the architectural vision and delivery of AI-powered loyalty systems across distributed engineering teams. Designs scalable APIs, data models, cloud services, and frontend architectures; solves complex technical problems; mentors engineers; drives modernization, operational excellence, and cross-functional alignment across Product, Data Science, and platform teams.

Conduct AI and machine learning research for next-generation wireless communication systems. Develop algorithms, build and evaluate models, run experiments, publish findings, and collaborate with research, development, and product teams to transition research into real-world technologies. The role also involves exploring emerging AI techniques, shaping technical direction, and contributing to Ericsson’s wireless technology strategy.

Leads Oracle Cloud Finance transformation workstreams, client engagements, and delivery teams across ERP and EPM programs. Designs finance processes, translates business requirements into Oracle Financials solutions, oversees configuration and deployment planning, and applies automation, analytics, and machine learning to improve finance operations. Manages risks, timelines, deliverables, and stakeholder relationships while coaching team members and identifying opportunities to improve budgeting, forecasting, reporting, and financial close processes.

Leads financial statement audits and evaluates governance, risk management, regulatory compliance, and internal controls for digital asset and financial services clients. Directs complex audit engagements, applies advanced auditing and technical accounting expertise, identifies material misstatements, and issues auditor reports. The role also involves client relationship management, coaching and developing diverse teams, communicating findings, and maintaining professional, independence, and ethical standards.

Leads large-scale technical programs for AI and ML platforms, coordinating researchers, engineers, business leaders, and cross-functional teams from concept through production. The role manages technical risks, delivery commitments, stakeholder alignment, and execution across distributed systems and real-time data platforms. It also establishes and grows the technical program management discipline, mentors TPMs, and guides senior leadership decisions in a highly regulated banking environment.

Develops and scales generative AI and automation solutions for memory circuit design, layout, verification, simulation, and tapeout workflows. Uses engineering and silicon data for predictive insights and optimization of floorplanning, placement, routing, and validation. Collaborates with design, CAD, product, manufacturing, and process teams to reduce manual effort, improve quality, and accelerate time-to-tapeout. Requires production-quality Python development and experience applying machine learning, data analysis, or automation to engineering workflows.

Define validation strategies, coverage architectures, and scalable methodologies for next-generation data center SSDs. Analyze requirements, architectures, workloads, and validation gaps; develop risk-based coverage models; evaluate emerging technologies; and apply AI, analytics, automation, and LLMs to improve validation effectiveness. Partner across architecture, firmware, systems, product, and validation teams, present strategic recommendations, influence technical direction, and mentor engineers.

Leads venture investment sourcing, evaluation, diligence, and execution across Seed through Series B. Drives company-wide AI and machine learning initiatives, maintains portfolio relationships, and develops executive, Board, and Investment Committee recommendations. The role requires technical fluency in AI or semiconductors, financial acumen, senior stakeholder influence, strategic judgment, team mentorship, and the ability to operate effectively in ambiguous, fast-paced environments.

Designs and operates secure cryptographic services for a multi-tenant SaaS platform, including key management, PKI, certificate lifecycle, secrets distribution, and workload identity. Applies ML and statistical techniques to detect cryptographic misuse and anomalies. Responsibilities include Kubernetes operations, CI/CD, incident response, threat modeling, secure coding, code review, compliance with FIPS and regulated-environment requirements, and collaboration across product, architecture, and engineering teams.

Own lab infrastructure operations, including system bring-up, equipment deployment, platform validation, configuration, maintenance, automation, and troubleshooting. Support server, GPU, FPGA, AI, memory, and environmental test platforms while enabling workload characterization, trace analysis, performance testing, and customer activities. Develop Python or shell automation, manage assets and calibration, and resolve hardware, firmware, software, networking, power, and thermal issues across global engineering teams.

Leads client-facing generative AI and data modernization engagements, directing solution design, delivery planning, architecture, and technical decisions. Guides teams in prompt engineering, context engineering, agent orchestration, LLM evaluation, RAG, vector databases, and AI guardrails. Oversees cloud AI implementations using Gemini, Vertex AI, AWS Bedrock, or Azure AI Services; develops Python prototypes; evaluates model performance; manages workstreams and deliverables; and mentors staff on AI methods, agile delivery, and technical problem-solving.

Designs and optimizes AI and machine learning solutions, transforms data into insights, and develops production AI systems. Responsibilities include building data infrastructure, speech recognition and NLP tools, and reusable evaluation and monitoring capabilities. The role independently tests AI systems for performance, robustness, bias, explainability, and failure modes; evaluates technical controls; documents findings; communicates recommendations to clients; and mentors junior team members.

Leads the design, development, testing, and evaluation of AI and machine learning solutions. Directs data analysis, modeling, infrastructure, monitoring, and integration efforts while establishing standards for AI robustness, bias, explainability, failure modes, and technical controls. Manages large projects, advises clients, coaches teams, and develops reusable evaluation tools and evidence workflows for responsible AI deployment.

Leads engineering teams delivering scalable AI and machine learning solutions on Google Cloud. Oversees generative AI architecture, Vertex AI, LangChain, LangGraph, LLM APIs, RAG, experimentation, evaluation, guardrails, and LLMOps. Guides technical strategy, client alignment, delivery execution, and team development while applying Python, data science, and machine learning methods to build reliable AI products.

Design and deploy AI systems and machine learning models, perform data wrangling and analysis, and integrate solutions into data infrastructure. Develop scalable AI applications, relational data systems, evaluation tools, automated tests, and monitoring capabilities. Assess AI behavior, robustness, bias, explainability, drift, hallucinations, and guardrails while evaluating technical controls and communicating findings to clients and stakeholders.

Leads Google Cloud AI and machine learning engineering teams and client workstreams. Oversees generative AI, LLM application, agent orchestration, retrieval-augmented generation, model evaluation, prompt engineering, guardrails, and LLMOps initiatives. Reviews architecture, code quality, testing, and solution delivery using Vertex AI, LangChain, LangGraph, Python, and LLM APIs. Coaches team members, improves reusable delivery assets, and manages client relationships, timelines, quality, and expectations.

Develop and implement AI and machine learning solutions, including algorithms, data models, infrastructure, evaluation tools, automated tests, monitoring, and responsible AI controls. Lead testing for robustness, bias, drift, explainability, hallucinations, and failure modes. Manage client accounts and technical workstreams, review evidence and controls, communicate trade-offs, mentor teams, and maintain delivery and professional standards.

Leads client-focused generative AI and data modernization architecture, translating business needs into scalable solutions using Google Cloud, Vertex AI, LLMs, vector databases, and modern data platforms. Advises stakeholders, develops AI evaluation and guardrail practices, guides experimentation and deployment, presents technical recommendations, supports proposals, and coaches teams through complex transformation programs.

Build cloud-based AI and machine learning solutions for intelligent enterprise services. Collaborate with developers, product managers, product owners, and quality engineers to design, implement, test, and deliver scalable software. Apply AI/ML techniques, high-performance algorithms, object-oriented programming, modular design, unit testing, profiling, and code optimization to improve enterprise workflows and user experiences.

Identify enterprise AI opportunities, evaluate emerging models and platforms, build prototypes and proofs-of-concept, quantify business value, and present recommendations to technical and executive stakeholders to drive AI adoption across the company.

Lead applied AI and data science work for recommendation and personalization systems. Partner with data scientists, engineers, and product managers to deliver customer-facing products; develop, train, evaluate, validate, and implement machine learning models; analyze large-scale numeric and textual datasets; apply statistical modeling, deep learning, causal inference, reinforcement learning, and recommender-system techniques; and communicate technical findings to business stakeholders while developing team talent.

Lead an Applied AI data science team developing large-scale personalized recommendation and machine learning solutions. Partner with software engineers and product managers, build models through design, training, validation, and deployment, analyze massive structured and unstructured datasets, evaluate emerging technologies, and translate complex findings into business outcomes while developing team talent.

Build infrastructure and tooling for AI evaluation, including distributed execution, task-set validation, graders, human-review calibration, statistical reporting, online feedback loops, and offline-to-online analysis. Partner with product, engineering, data, and ML teams to create reliable evaluation workflows that support faster, statistically sound AI product decisions.