Machine Learning Jobs in San Jose, CA

Lead pre-sales efforts for Media & Entertainment customers: present Snowflake to executives and technical audiences, deliver demos and POCs, design solutions, collaborate with product and engineering, and close large deals while maintaining deep industry and competitive knowledge.

As an Applied AI Engineer at Luminai, you will design experiments, optimize machine learning models, and enhance AI platforms for healthcare automation.

The role involves improving demand forecast accuracy, implementing ML models, and collaborating with various teams to analyze market trends.

Lead strategic positioning and integration of AI-driven and digital platforms for semiconductor process development. Define end-to-end solutions, orchestrate insight integration, partner with product and engineering, identify gaps and high-value AI use cases, and drive adoption and enablement across internal teams and customers.

Develop and evaluate data science modeling algorithms using sensor data, collaborate with SW engineering on data pipelines, and fine-tune algorithm performance while documenting processes.

Develop and evaluate machine learning algorithms using time-series sensor data, integrate data pipelines with software engineering, and communicate technical information globally.

Lead a Customer Success team, manage key semiconductor accounts, drive customer adoption, and translate technical challenges into solutions for strategic growth.

The Data Engineer will gather and process large datasets, develop algorithms, conduct data analysis, and improve predictive models for business decisions.

The Data Engineer will gather and process data, analyze trends, support business decisions, and collaborate on predictive models while improving algorithms and efficiency.

Conduct research and implement deep learning and computer vision models (detection, ID, tracking, few-shot learning). Build proofs-of-concept with customers, commercialize solutions, publish papers and patents, and collaborate with production teams to deliver robust Industrial AI systems.

The Staff Applied Scientist will develop machine learning models for forecasting, collaborating with cross-functional teams to improve advertising campaigns, and drive model development using data.

Lead a major product area end-to-end: set strategy and KPIs, define customer personas and market thesis, prioritize features, drive execution with engineering, and align cross-functional stakeholders to deliver high-impact AI-enabled hiring and people-analytics solutions.

Lead the Platform Software Engineering team building and scaling core data infrastructure for research, market-making, and risk platforms. Hands-on role combining individual contributor work, system architecture, cross-functional project leadership, and people management while collaborating with Research and Business Operations.

Develop and optimize state-of-the-art deep learning and ML techniques for current and next-generation GPUs. Analyze and tune performance, provide GPU-based AI solutions to customers, and collaborate with architecture, research, libraries, tools, and system software teams to influence future hardware and software designs.

Build and maintain backend APIs and data pipelines that ingest and process large-scale construction data (360° video, schedules, IoT). Optimize storage and query performance using DuckDB and PostgreSQL, extend Airflow workflows, integrate Wasm for local computation, monitor data quality, and collaborate with frontend, CV, and ML teams to deploy production models and analytics.

The Forward Deployed Research Scientist engages directly with AI labs to develop data strategies, fine-tune models, run experiments, and collaborate on publishing research, all in a fast-paced client-focused setting.

Lead individual-contributor responsible for designing, coding, and deploying deterministic and stochastic supply network simulations and AI/ML augmentations to forecast long-lead procurement. Audit large datasets, translate outputs into VP-level recommendations, modernize legacy planning into automated high-performance scripts, and partner with supply chain, procurement, finance, and engineering to optimize sourcing and material forecasting.

Lead development of AI enablement roadmap, build state-of-the-art AI systems, and produce tools, services, and blueprints to accelerate AI adoption across NVIDIA. Collaborate with cross-organizational leaders and external partners to optimize productivity for software, hardware, and other workflows.

Design and implement AI-driven optimizations for software development workflows, leveraging LLMs and Agentic AI to enhance productivity and reliability.

Design, build, and own full-stack web and mobile applications and backend infrastructure. Architect scalable APIs, databases, and orchestration services, integrate and productize ML/AI models, collaborate with design and data science, and drive the software lifecycle from prototype to production.

Design, build, and own web and mobile applications and backend infrastructure. Implement APIs, database schemas, and scalable microservices. Integrate and productize ML/DL models, leverage code-generation tools, and collaborate with design, data science, and aviation experts to deliver production-ready SaaS features.

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.

Lead engineering teams building analyzers and debuggers (e.g., Intel System Debugger, VTune) to enable observability, profiling, and performance analysis. Define strategy, collaborate across HW/SW, drive technical excellence, mentor engineers, and deliver products for AI/HPC and developer communities.

Design, deploy, and maintain end-to-end ML risk solutions at scale to detect and prevent fraud, merchant risk, and credit loss. Partner with cross-functional teams, lead technical decisions, build ML tooling, monitor models in production, and investigate emerging abuse patterns to improve detection and decisioning.