Machine Learning Jobs in Santa Clara, CA

Lead executive product vision and multi-year roadmap for CrowdStrike's Endpoint Protection Platform. Own AI-native strategy, EPP/EDR/XDR roadmap, partner ecosystem, and executive representation. Build and scale senior product teams, align cross-functional stakeholders, manage budget, and drive platform extensibility, telemetry, detection, and automated response at global scale.

The Staff Software Engineer will lead technical projects on the Secondary Driving System, focusing on software architecture, multi-sensor perception, and integration, while mentoring the engineering team.

Lead design and deployment of enterprise-scale multi-agent AI systems across the semiconductor lifecycle. Define strategy, architect agentic AI platforms (LLMs, RAG, knowledge graphs), identify automation opportunities, establish AI governance, integrate with EDA and operations systems, mentor technical leaders, and represent the company externally.

Ten-week, full-time paid Data Science internship (June–August 2027) working on machine learning, deep learning, and large-scale data analysis. Interns evaluate modeling tools, integrate internal and external data/APIs, design data science models, and present insights to stakeholders. In-person attendance at an assigned U.S. location is required; interns may be considered for full-time roles after graduation.

Ten-week, full-time paid Data Science internship for current PhD students. Interns will build and evaluate modeling and analytics tools, integrate internal and external data sources, design data science models, and communicate results. Candidates must continue their PhD after the internship and may be evaluated for full-time roles upon graduation.

Designs and delivers multi-modal technical learning programs for Sunnyvale engineering teams. Leads needs analysis, partners with senior technical stakeholders, facilitates training, owns program evaluation, and shapes technical development practice across sites.

The Director of Developer Relations leads the strategy for engaging developers around CoreWeave's AI SaaS offerings, creating content, fostering community, and improving the onboarding experience.

The Senior Product Marketing Manager will position CoreWeave's SUNK offering, develop messaging, and collaborate with engineering and sales teams to enhance product adoption and market understanding for AI training workloads.

Lead and deliver large-scale AI/ML programs and the next-generation Generative AI platform. Build and grow a world-class TPM discipline, manage cross-functional delivery, mitigate technical risk, and drive execution across product, engineering, design, and data science to achieve business impact in regulated environments.

Build and maintain scalable, reliable ML data platform systems to support dataset generation, model training, analytics, monitoring, and large-scale inference. Collaborate with ML, software, and infrastructure engineers to design cost-efficient data lake and training infrastructure using vendor and open-source tools to enable next-generation ML models.

As a Manager in Data Science, you'll lead the development of machine learning models, partner with engineering and product teams, and analyze large datasets to enhance customer experiences and prevent fraud.

Design, deploy, and maintain end-to-end ML-driven risk solutions at scale to detect and prevent fraud, abuse, and credit risk. Lead technical decisions, build ML tooling and processes, apply state-of-the-art models and third-party data, investigate emerging risk patterns, and collaborate with platform and cross-functional teams to ensure reliable real-time model operation.

As a Sr. Staff Machine Learning Engineer, you will drive the development of cloud security features, leveraging AI/ML to solve real-world challenges and optimize production systems.

The role involves leading data science initiatives, building machine learning models, conducting experiments, and collaborating with teams to enhance customer experiences and business outcomes.

Lead an Agile team to design, build, deploy, and maintain production-scale ML models and pipelines. Drive ML architecture, model training, CI/CD, monitoring, and cloud/Kubernetes-based deployments while ensuring responsible, explainable, and well-governed ML solutions.

Lead design, build, and productionize scalable ML models and pipelines. Collaborate with product and data science teams, implement cloud-based architectures, ensure CI/CD, monitoring, model governance, and responsible AI practices.

Lead ML engineer responsible for designing, building, deploying, and monitoring production ML systems at scale. Collaborate with product and data science teams, develop ML architectures and data pipelines, apply CI/CD and cloud-based MLOps, retrain and govern models, and ensure performance, reliability, and responsible/explainable AI practices.

Develop and deploy machine learning solutions for autonomous vehicles, mentoring engineers and collaborating on model implementations.

Design, build, and operate production ML decision systems to detect and prevent payment fraud, account takeover, scams, and other abuse. Integrate diverse signals into low-latency serving and batch scoring, own feature pipelines and model lifecycle, develop AI-assisted triage and feedback loops, and partner cross-functionally to balance fraud reduction with legitimate customer access.

Design, build, and operate production ML systems that generate trusted signals for ranking, retrieval, recommendations, propensity/churn/LTV, and next-best-action decisioning. Define signal/data contracts, own feature and candidate generation through serving, experimentation, monitoring, and feedback loops, and evaluate long-term business impact, trust, fairness, and compliance. Partner across product, data, modeling, risk, and compliance and apply AI/agents to accelerate engineering and operations.

Design, build, and productionize large-scale foundation models and ML systems (transformer architectures, self-supervised learning). Architect and implement training, data pipelines, deployment, monitoring, and CI/CD. Collaborate with cross-functional teams, ensure model governance, reliability, and performance, and apply ML engineering best practices at scale.

The Senior ML Infrastructure Engineer will design, implement, and optimize computing platforms for simulation workflows, ensuring high reliability and performance. Responsibilities include leading technical decisions, collaborating with ML engineers, and enhancing system efficiency.

As an AI/ML Engineer, develop and optimize analytics frameworks to enhance autonomous vehicle development, focusing on automation tools and collaboration with engineering teams.

The VP of Product will lead CoreWeave's Research Training Stack, focusing on enhancing AI infrastructure, ensuring quality for pre-trained models, and connecting researchers with advanced cloud capabilities.

Lead architecture and technical strategy for AI-driven product quality systems using LLMs and agents. Build scalable evaluation frameworks, detect regressions, generate insights, and drive cross-functional adoption while mentoring engineers and defining standards for trustworthy AI.