Machine Learning Jobs in Sunnyvale, CA

Design and build scalable backend services for CrowdStrike’s cybersecurity risk intelligence platform. Responsibilities include architecting microservices, APIs, databases, real-time data pipelines, monitoring, and remediation automation; optimizing distributed systems; ensuring reliability; collaborating across teams; and mentoring engineers. The role requires extensive experience with Golang or Python, cloud infrastructure, Kafka, databases, AI technologies, and high-volume distributed systems.

Build and scale reliable agentic AI systems, including orchestration, code execution, agent memory, LLM self-improvement, simulations, knowledge graphs, and multimodal interfaces. Implement AI algorithms, productionize them, optimize performance, write robust code, contribute to technical roadmaps, collaborate across engineering and product teams, and mentor engineers. The role requires strong production-systems experience, technical communication, operational excellence, and programming in Python, Golang, and/or Java.

Leads software product management across autonomy, simulation, platforms, cloud tools, and fleet operations. Builds and manages the product team, owns software vision and roadmap governance, defines commercial strategy, and oversees products from research through deployment and continuous improvement. Partners with engineering, AI, hardware, UX, safety, compliance, customers, and vendors on architecture, testing, release decisions, data strategy, lifecycle management, and build-versus-buy choices. Requires deep software product expertise, robotics or autonomous systems experience, cloud-to-edge knowledge, and extensive leadership experience.

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.

Lead product development for an enterprise agentic AI platform and ServiceNow catalog interpreter from concept through launch. Define product specifications, technologies, developer interfaces, user experiences, success metrics, and roadmap priorities. Partner with engineering, machine learning, design, customer success, and deployment teams; analyze product usage data; support developer ecosystems and hackathons; and build relationships with enterprise CIOs and IT leaders.

Own the Tool Studio product area for an agentic AI platform, driving development from concept through launch. Define product strategy, specifications, success metrics, developer interfaces, and end-user experiences. Partner with engineering, machine learning, design, deployment, and customers to prioritize roadmaps, improve integrations, support adoption, and build the developer ecosystem. Use product usage data, market research, and customer feedback to guide decisions while building trusted relationships with enterprise technology leaders.

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.

Build and scale Moveworks’ generative AI conversation engine for enterprise users. Design scalable APIs and infrastructure supporting multiple chat channels, optimize latency, memory usage, throughput, and multilingual capabilities, and create logging, tracing, debugging, and monitoring systems. Collaborate with machine learning, application engineering, product, and customer support teams while promoting strong engineering practices and rapidly delivering innovative solutions.

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.

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.

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 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.

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.

Develop and deploy machine learning and LLM capabilities for agentic search, query understanding, search planning, and retrieval. Build evaluation datasets, improve model behavior through experimentation and fine-tuning, and optimize quality, latency, cost, and reliability. Collaborate across search, product, infrastructure, and AI teams while owning projects through production and mentoring engineers.

Develop and implement advanced machine learning algorithms and models for Atlassian products. Design system and model architectures, conduct experiments and evaluations, collaborate with product, engineering, and analytics teams, and mentor emerging machine learning engineers. The role also contributes to shared AI infrastructure and capabilities supporting search, knowledge discovery, and conversational experiences across the company.

Develop and deploy scalable machine-learning solutions, particularly deep-learning forecasting models for company top-line metrics. Lead the full ML lifecycle from problem definition and experimentation through production deployment and improvement. Design production-grade architectures, evaluate and improve models, collaborate with cross-functional teams, explain technical concepts to varied audiences, and mentor junior engineers.

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.

Design and implement AI-powered agent builder features and scalable web applications for the Moveworks Agentic Assistant ecosystem. Build web technology stacks including authentication, event tracking, application platforms, design systems, and backend APIs. Collaborate with product, machine learning, and engineering teams to improve user experiences, support global expansion, and drive customer adoption.