Machine Learning Jobs in San Jose, CA

The Senior Staff Physical AI Data Algorithm Engineer is responsible for designing and optimizing a vehicle-cloud data closed-loop architecture, managing data toolchains, and ensuring high-quality data standards throughout the model development cycle.

Lead the architecture design and R&D in synthetic data and simulation systems for autonomous driving and robotics, ensuring high-quality data generation and closed-loop simulations.

Lead the architecture and deployment of LLM-powered conversational chatbot systems, optimizing performance and ensuring scalability in a crypto environment.

Develop efficient ML solutions for perception including sensor fusion, obstacle detection, scene understanding, and robust onboard systems.

Design, implement, and maintain full-stack software for advanced robotic platforms: scalable backend services, responsive frontends, secure APIs, hardware integration, data pipelines, dashboards, and ML model integration. Collaborate across teams, participate in design reviews, and translate requirements into robust, maintainable robotic software solutions.

Lead the data and ML product vision: define roadmap, prototype models on noisy behavioral datasets, translate signals into user-facing features, partner with engineering to productionize ML, and generate insights to inform product roadmaps.

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.

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.

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.

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.

Design, build, and operate production ML signal systems—ranking, retrieval, recommendations, propensity, and next-best-action—covering feature/candidate generation, serving, experimentation, monitoring, and feedback. Define signal contracts (freshness, provenance, confidence), evaluate long-term impact (trust, fairness, compliance), and partner across product, data, and risk teams to deliver reusable customer-intelligence capabilities.