Machine Learning Jobs in Santa Clara, CA

Lead and deliver compiler software programs across multi-functional, global teams. Drive projects using Agile and Waterfall, define and improve processes, escalate risks, report status to executives, and resolve technical and resource conflicts to meet release goals.

Lead the product vision for innovative AI technologies, translating research into marketable products, and collaborating with cross-functional teams.

Seeking a Research Scientist to develop AI models for enhancing 3D reconstruction processes, requiring strong skills in computer vision and neural reconstructions.

The Applied Scientist will transform raw data into actionable insights by developing machine learning models for personalization, ranking, and optimization to enhance conversion and revenue. This role emphasizes building production systems, frameworks for experimentation, and communication with team members regarding technical challenges.

The role involves incorporating machine learning techniques into sensor simulation, working with teams to develop effective models for Lidar, Radar, and Camera systems.

Lead development and execution of a multi-year automation and AI/Industry 4.0 roadmap for manufacturing. Build and mentor a multidisciplinary automation team, oversee end-to-end program delivery (concept through commissioning), integrate ML/vision/IoT/SCADA systems, manage budget and metrics, and drive measurable improvements in safety, quality, throughput, and cost.

The Demand Planner is responsible for improving forecast accuracy, analyzing market trends, and collaborating across functions to support supply chain effectiveness and strategic decision-making.

Lead reliability engineering for 2.5D/3D advanced semiconductor packages: perform physics-of-failure modeling, define and execute stress tests, manage OSAT qualifications, select and qualify materials, perform failure analysis (FIB/SEM/TEM/X-ray), drive root-cause and corrective actions, and collaborate with design, process, and suppliers to improve package reliability and manufacturability.

Lead design and development of scalable, high-performance ML training infrastructure. Drive distributed training performance optimization, observability, and developer experience. Own cross-functional infrastructure initiatives, set technical direction and standards, and mentor engineers to deliver platform capabilities that support large-scale model training.

In this role, you will lead AI personalization initiatives, develop AI/ML models for user experience, collaborate across teams, and ensure rigorous development practices.

Lead technical engagement with MedTech customers to design and deploy AI and accelerated computing solutions. Develop proofs-of-concept, diagnose AI stack performance issues, collaborate with cross-functional teams, document and teach best practices, and build strategic partnerships to drive AI adoption in healthcare.

Lead the unified Search and Voice product organization to define multi-year strategy and roadmap for the Roku Entertainment Assistant. Drive prioritization, cross-functional alignment, monetization, AI/ML innovation (LLMs, RAG, conversational AI), experimentation, quality, compliance, and measurable engagement and revenue outcomes while growing and mentoring a high-performing PM team.

The Solutions Engineer will engage with stakeholders to design AI-powered engineering solutions, bridging business needs and technical execution while guiding customers through AI integration.

Seeking a Distinguished Technologist to lead AI architecture and design for HPE's Private Cloud AI offerings, focusing on applied AI, agentic AI, and generative AI. Responsibilities include technical leadership, implementation, and mentoring engineers while optimizing AI systems for cloud-based solutions.

Develop and validate advanced driver assistance and autonomous driving systems, ensuring compliance with regulatory requirements while mentoring junior engineers.

The Technical Program Manager will lead software programs, collaborate with multiple teams, manage dependencies, and drive product delivery to improve engineering velocity and transparency.

Manage strategy creation, sales account management, and relationship building for Akamai. Drive revenue growth and improve customer relations in the telecommunications sector.

The role involves developing quantization and compression strategies for AV models, conducting numerical sensitivity analyses, and creating automated tooling for low-precision deployments, emphasizing safety and performance.

You will own the compilation stack transforming AI models into optimized inference for autonomous driving. This includes developing tools and techniques for model export and optimizing performance while ensuring safety and reliability.

The Senior Compiler Engineer will develop a model compilation toolchain for deploying optimized machine learning models in autonomous vehicles, focusing on performance engineering and collaboration with cross-functional teams.

Lead end-to-end ML and perception efforts for future sensing in autonomous vehicles: design sensor architectures, multi-modal fusion, model development, metrics, simulation-driven evaluation, system integration, and production readiness while mentoring engineers and building scalable data and training infrastructure.

Develop and evaluate ML and perception models for future sensing and autonomous driving. Work on detection, tracking, classification, sensor fusion, training pipelines, validation, robustness metrics, and integrate models into onboard systems while collaborating with cross-functional teams.

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 operate production ML decision systems to detect and prevent payment fraud, account takeover, identity abuse, merchant/marketplace risk, scams, and other adversarial activity. Own end-to-end production lifecycle: data contracts, low-latency inference, batch scoring, feature quality, deployment, monitoring, incident response, rollback, and feedback loops. Develop AI-assisted workflows and reusable decision capabilities while partnering with modelers, product, compliance, and operations.