Machine Learning Jobs in San Jose, CA

Lead technical strategy and architecture for application security across WAF, WAAP, DDoS, API security, bot defense, TLS, identity, and AI/ML detection. Drive modernization to cloud-native, SaaS, and API-driven services, enforce secure-by-design patterns, mentor senior architects, perform threat modeling, and align cross-functional teams on compliance, telemetry, and scalable runtime defenses.

Analyze and reduce RTL and pre‑silicon power for GPUs and networking chips using EDA tools and AI/ML. Run workloads, perform comparative power analysis, prototype Verilog features, debug power anomalies, and collaborate with architects, RTL designers, and physical design teams to drive power improvements.

Lead strategic GTM and co-selling initiatives with Google Cloud to drive NVIDIA AI adoption. Develop and maintain technical enablement assets, run developer activations and events, collaborate with Google Cloud field and engineering teams, convert samples into demos and labs, and translate customer feedback into product and roadmap insights.

Develop reusable ML tooling and libraries, collaborate with data scientists to productionize models, build services and APIs (Docker/Linux), follow engineering best practices, and support project lifecycle from intake to maintenance.

Join the AI team to develop and deploy real-time perception and deep learning models for autonomous vehicles. Build and optimize data pipelines, research object detection, segmentation, road understanding, motion prediction, and apply multi-modal sensor fusion for robust onboard perception.

The Software Engineer, Perception develops autonomous aircraft systems to detect and manage wildfires using real-time data processing and machine learning techniques.

Develop data-analysis pipelines bridging ultrasound, synthetic biology, imaging and sequencing. Collaborate with wet-lab and ML teams to define objectives, encode priors, run ML pipelines for de-novo design and closed-loop active learning, and produce actionable insights to accelerate molecule discovery and device optimization.

As a Founding AI Engineer, you will develop an AI-powered knowledge platform, focusing on backend systems, machine learning data pipelines, and optimizing tech stack for performance while participating in the entire product lifecycle.

The role involves developing new methodologies for advertising conversion modeling using advanced machine learning techniques, supporting various quantitative research aspects. Candidates should have extensive experience in statistical and deep learning methodologies, contribute to research, and build generative models for ad optimization.

The Senior B2B Marketer will develop and execute marketing strategies, collaborate with Sales, create marketing materials, conduct research, and optimize marketing effectiveness to drive growth in the gaming and 3D industries.

Lead the Seller User Experience team, focusing on enhancing customer satisfaction through innovative technology solutions and user feedback. Collaborate with stakeholders and mentor teams to achieve superior user experiences.

You will design and implement high-performance software while applying machine learning techniques to complex problems in electronic design automation.

Lead strategic ISV partnerships to integrate NVIDIA software and SDKs into partner data platforms. Drive technical and GTM alignment, influence product strategy, recruit and lead cross-functional teams, promote NVIDIA solutions to architect and developer communities, and surface partner needs to internal product and engineering teams.

Design and develop scalable, high-performance data and API infrastructure for real-time processing. Mentor engineers and collaborate with teams to enhance AI model evaluations.

Responsible for designing experiments to evaluate AI models, developing evaluation methodologies, analyzing performance data, and collaborating on production insights.

Designs, builds, and evaluates language models for AI security products. Responsibilities include dataset curation, preprocessing, LLM experimentation, safety and robustness evaluation, dashboards, knowledge graphs, model fine-tuning, production integration, monitoring, CI/CD, and ML tooling. Collaborates with researchers and data scientists to automate quality assurance and guardrails while delivering reliable, scalable, and interpretable models.

Develop and maintain Android applications for embedded devices; prototype and debug on hardware (Raspberry Pi, NVIDIA); work with Yocto build systems, Qt/QML, and Git/Stash. Preferred experience with web frameworks, iOS, AR/VR, and machine learning. Collaborate with team, communicate effectively, and support embedded graphics/media and RTOS environments.

Design scalable data analytics platforms, make architectural decisions, collaborate with analysts, mentor developers, and assess technologies for a Big Data Platform.

The CTO will lead Fathom's technical strategy, focusing on AI governance, building partnerships, defining standards, and advancing AI safety practices.

The role involves developing AI applications for VLSI design using machine learning and related technologies. Duties include infrastructure design, AI solution implementation, collaboration with teams, and maintaining design databases.

Manage and grow sales strategies and customer relationships for proteanTecs in the semiconductor industry, ensuring long-term success by aligning solutions with customer needs.

The Director of Application Engineering leads a team of engineers, manages customer support, trains clients on tools, and collaborates with R&D to enhance product features.

As a Software Engineer, you'll develop software solutions for semiconductor equipment, lead projects, troubleshoot complex issues, and collaborate with customers on requirements.

Lead AI/ML safety engineering for Level 4 autonomous vehicles. Define SOTIF and functional safety strategies, derive requirements for learned systems, design runtime safety monitors and OOD detectors, assess dataset coverage and bias, analyze ML failure modes, and build evidence-backed safety cases. The role requires collaboration across perception, prediction, planning, systems, and safety engineering teams, with onsite work five days per week in Santa Clara, California.

Develop and deploy machine learning planning algorithms for autonomous trucks. Own the full ML lifecycle, including data curation, feature engineering, training, evaluation, deployment, monitoring, and improvement. Design trajectory and behavior-planning models, safety guardrails, validation pipelines, and performance metrics. Investigate failures and edge cases, optimize inference, and collaborate with perception, prediction, mapping, runtime, and systems teams to deliver reliable production systems.