Machine Learning Jobs in Santa Clara, CA

Build infrastructure and tooling for AI evaluations, including distributed scoring engines, task-set validation, grader systems, human-review calibration, statistical leaderboards, online feedback capture, and offline-to-online evaluation loops. Partner with product, engineering, data, and ML teams to deliver reliable, observable evaluation workflows that support faster and more trustworthy AI product development.

Design and deliver enterprise-scale AI systems, including foundation model training, LLM inference, agentic workflows, vector search, guardrails, evaluation, governance, and observability. Set architecture and optimization strategies for scalable, cost-efficient, responsible AI platforms. Lead model routing, caching, orchestration, and cloud deployments while mentoring engineers, influencing stakeholders, and advancing AI research into production.

Develop production LLM-powered agents and machine learning solutions for customer risk identification and threat detection. Translate threat-research methods into agent logic, build data requirements and pipelines, evaluate precision and recall, backtest and tune models, and deploy and monitor reliable solutions using CI/CD. Collaborate with threat researchers and data engineers while addressing production reliability, observability, data quality, cost, latency, and accuracy.

Build scalable, interactive enterprise applications and reusable tools, libraries, and frameworks. Develop backend and frontend systems using Java, JavaScript, Python, AI/ML, conversational AI, and large-scale data technologies. Design and deliver new features, improve existing products, enforce coding and testing standards, collaborate with product owners, and mentor colleagues.

Leads the DLP engineering team responsible for high-throughput, low-latency inspection engines and distributed incident-processing systems. Oversees architecture, delivery, team growth, dictionary compilers, pattern matching, NER and ML model integration, concurrency, IPC, performance optimization, production troubleshooting, forensic logging, and SIEM/SOAR integrations. Requires advanced C systems programming on Linux or FreeBSD, deep DLP expertise, and experience managing high-performing engineering teams.

Develop advanced threat detection capabilities, custom alert logic, threat-hunting exercises, and real-time incident response processes. The role collaborates with Red and Purple Teams, researches emerging threats, improves security visibility, contributes to security architecture and controls, and supports security assessments. Responsibilities include Kubernetes-focused detection, multiple query and programming languages, endpoint event analysis, incident containment, remediation, and occasional after-hours on-call participation.

Develop data science solutions for payments across Square and Cash App. Build AI-powered tools, ETL pipelines, dashboards, machine learning models, experiments, and cloud data systems. Partner with Product, Engineering, Finance, and Operations to improve payment success, cost efficiency, risk mitigation, and decision-making. Translate business needs into practical solutions, research questions independently, and operationalize data processes and infrastructure.

Develop data tools, AI automations, ETL pipelines, dashboards, and machine learning models for payments. Analyze experiments, optimize payment performance and costs, mitigate risk, maintain data systems and cloud infrastructure, and translate business needs into actionable solutions while partnering with Product, Engineering, Finance, and Operations.

Build and operate scalable full-stack software and AI-powered products for physical operations. Own technically complex areas, define technical strategy, develop backend services, APIs, frontend and potentially mobile experiences, and translate customer problems into measurable outcomes. Lead cross-team initiatives, mentor engineers, participate in technical reviews, and raise engineering quality and velocity.

Design, develop, deploy, and support large-scale AI systems, including foundation model training, LLM inference, agentic workflows, similarity search, guardrails, evaluation, governance, and observability. Establish enterprise AI architecture standards, optimize model performance and infrastructure costs, lead hybrid and multi-model orchestration, promote responsible AI, and mentor engineering teams while influencing technical strategy and research-to-production processes.

Develop and deploy advanced machine learning and data science solutions across large numeric and textual datasets. Partner with data scientists, engineers, and product managers to build customer-focused products. Lead model design, training, evaluation, validation, and implementation while researching emerging technologies and translating complex analyses into business outcomes.

Develop and implement AI solutions and machine learning systems that enhance products and solve complex client problems. Responsibilities include advanced analytics, LLM production deployment, prompt engineering, RAG pipeline optimization, cross-functional collaboration, technical discovery, responsible AI advocacy, client relationship building, and mentoring junior team members.

Leads Fenergo product consulting engagements focused on AML, sanctions, data analytics, and regulatory risk solutions. Manages client accounts, oversees project planning and execution, guides analytics initiatives, and develops data-driven business strategies. Uses predictive analytics, machine learning, statistical modeling, and scenario analysis to generate insights. Manages and mentors teams, engages stakeholders, resolves conflicts, promotes innovation, and ensures high-quality deliverables and adherence to professional standards.

Design and build large-scale mapping algorithms and geospatial data systems for automated map reconstruction and maintenance. Develop lane, road-network, geometry, topology, and map-conflation systems; process sensor and third-party data through distributed pipelines; apply machine learning and computer vision for feature extraction and change detection; and create automated validation and quality systems. Partner with perception, localization, simulation, and platform teams while mentoring engineers and driving technical problems into production.

Leads product strategy and roadmap execution for autonomous campus networking solutions spanning wired, wireless, security, cloud-managed, and data center environments. Translates customer challenges and operational data into AI-powered product capabilities, partners with engineering and architecture teams, develops business cases and go-to-market strategies, and drives innovation through prototypes, experimentation, automation, and analytics. Represents the company with customers, partners, executives, and industry stakeholders.

Owns analytics and optimization strategy for advertising revenue across Zynga’s mobile game portfolio. Responsibilities include monitoring performance, diagnosing trends, designing A/B tests, identifying monetization opportunities, partnering with product, analytics, and data science teams, supporting AI and ML models, leading cross-functional initiatives, and mentoring analysts.

Leads pricing and revenue data science initiatives for consumer markets clients. Develops predictive models, forecasts trends and customer behavior, manages dashboards and visualizations, ensures data quality, and translates complex analytics into strategic decisions. Oversees client engagements, validates outcomes, directs complex projects, and coaches diverse, high-performing teams.

Develop and deploy large-scale machine learning and personalization models for web and mobile customer experiences. Partner with data scientists, engineers, and product managers; analyze massive numeric and textual datasets; and manage model design, training, evaluation, validation, and implementation. Research advanced methods including foundation models, reinforcement learning, causal inference, transformer architectures, and recommender systems while translating technical findings into business outcomes.

Develop and deploy high-scale machine learning and personalization models for Capital One’s web and mobile experiences. Responsibilities include researching recommendation systems, foundation models, reinforcement learning, causal inference, and transformer architectures; analyzing large datasets; building models through training, evaluation, validation, and implementation; and collaborating with data scientists, engineers, and product managers.

Design, build, and operate scalable hybrid human/machine data labeling platforms for autonomous vehicles. Own end-to-end projects, define platform roadmap, integrate ML-driven annotation, build high-performance UIs/APIs, and collaborate across ML, product, and operations to improve labeling quality and velocity.

Conduct advanced AI and machine learning research for autonomous driving, designing and prototyping foundation models, generative AI, multimodal systems, and other modern ML approaches. Shape technical roadmaps, mentor researchers and engineers, collaborate across multidisciplinary teams, and represent GM through publications, patents, and conference participation.

Leads a cross-functional team developing behavior evaluation, human benchmarking, and behavioral inference capabilities for autonomous vehicles. Designs and deploys production ML models, establishes technical roadmaps, and integrates behavior critics into simulation-based validation, release decisions, and performance monitoring. Partners with autonomy, simulation, safety, and product teams to assess driving behavior against human expectations for safety, comfort, and intuitiveness.

Design, develop, and operate scalable Voice AI platforms, including audio acquisition, conversation management, speech generation, model integrations, and voice harnesses. Build distributed systems across regions and datacenters, develop LLM-based applications and agents, improve SRE tooling, enforce software engineering practices, collaborate with customers and product managers, and mentor engineering colleagues.

Design, develop, and deploy scalable Voice AI platforms and distributed systems across regions and datacenters. Build audio acquisition, conversation management, speech generation, model integrations, and voice harness capabilities. Partner with product managers and customers, improve SRE tooling, enforce software engineering best practices, and mentor engineering colleagues. The role requires extensive experience with LLM applications, AI agents, programming, Kubernetes, and highly available workloads.

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.