Machine Learning Jobs in San Jose, CA

Architect and optimize timing and power implementation flows for ASIC/FPGA designs. Lead STA and UPF-based power flows, library generation/characterization, automation infrastructure, and apply ML/AI to improve flow efficiency, debug productivity, and signoff QoR while collaborating across PD, STA, library, and CAD teams.

This internship involves developing machine learning approaches for biological data, designing experiments, collaborating on research, and presenting findings.

The Senior Machine Learning Engineer will design, deploy, and operate ML systems for advertising, collaborating with cross-functional teams to enhance performance and reliability. Responsibilities include mentoring, promoting best practices, and owning ML project lifecycles.

As a Senior Software Engineer at NVIDIA, you will design and implement inference software optimizations for AI applications using TensorRT, collaborating with deep learning experts to influence hardware and software design.

Develop and implement image processing and machine learning algorithms, collaborate with engineers, and demonstrate algorithm performance for semiconductor process development.

The role involves optimizing Small Language Models for NVIDIA's G-Assist, enhancing context-aware features, and ensuring system performance while collaborating with engineering and product teams.

Lead software initiatives and manage engineering programs for Gen AI models, coordinating teams, improving processes, and reporting to senior management.

The role involves designing end-to-end perception systems for autonomous driving, developing deep learning models, and optimizing data-driven development for improved accuracy and robustness.

Develop and evaluate quantization and sparsity recipes for LLM inference, run large-scale post-training and quantization-aware experiments, build evaluation and numerics-debugging tools, collaborate with inference framework teams, and publish or upstream improvements to production inference libraries.

The role involves designing and implementing AI agents and infrastructure for semiconductor design, ensuring efficient evaluation and deployment of AI solutions.

Develop and maintain visual analytics tools for AI/ML workloads, collaborating on visualization solutions and innovating methods to enhance workflows.

This role involves developing deep learning algorithms for autonomous driving, optimizing neural networks, and leveraging multimodal datasets for reinforcement learning and decision-making.

Develop leading machine learning frameworks for NVIDIA Earth-2, validate applications, and implement new ideas in deep learning for weather and climate AI.

Responsible for designing and optimizing VLSI circuits, translating requirements into data science problems, and building machine learning solutions. Collaborate on hardware design and integrate models into existing tools.

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

The Machine Learning Scientist will lead open-source research, design experiments, develop novel methodologies, and analyze large-scale data to advance AI model evaluation and transparency.

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.

Lead development of scalable molecular dynamics pipelines, integrating physics-based models with machine learning frameworks to enhance molecular engineering and experimentations.

The ML Research Scientist will design Bayesian optimization frameworks, collaborate with lab scientists, and integrate ML models with experimental data.

The role involves designing de novo frameworks for molecular engineering, collaborating across teams to optimize data-driven biological experiments and integrating ML models with experimental systems.

Develop algorithms and optimizations for inference and compiler stack, enhance performance metrics, collaborate with hardware teams, and publish work on novel approaches.

As a Senior Computer Vision Engineer, you will develop and optimize computer vision and machine learning algorithms for robotics systems, while collaborating with cross-functional teams and maintaining code documentation.

The Solutions Architect will assist customers in adopting NVIDIA's GPU hardware and software, deploying AI/ML solutions, and engaging with technical teams and customers.

Design and develop Bird’s Eye View fusion models for autonomous driving using multimodal sensor inputs like LiDAR, focusing on performance, robustness, and scalability.

The Senior ML Performance Engineer will architect a Performance Testing Platform, optimize ML performance on GPUs, and validate model efficiency.