Machine Learning Jobs in San Jose, CA

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.

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.

The role involves solving customer problems by building ML solutions, engaging with various stakeholders, and taking ownership of the outcome through deployment.

The role involves building production ML models for voice AI, focusing on fine-tuning, deploying, and evaluating models to enhance human-like interactions.

Design, develop, and deploy production-grade machine learning models and pipelines. Preprocess and analyze large datasets, collaborate with cross-functional teams, monitor model performance, and implement scalable ML solutions to improve customer experiences.

The AI/ML Engineer will design and deploy advanced AI models, optimize performance, and directly engage with government officials to transform industries through innovative AI solutions.

The intern will support the data science team by gathering data, performing exploratory analysis, building machine learning models, and developing visualizations to aid business insights.

Develop and apply deep learning and data analysis techniques to create scalable vehicle simulation models, collaborating with teams to advance autonomous technology.

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.

Develop datasets, models, and evaluation systems for content safety, product security, robustness, and ML fairness for multimodal LLMs. Research and implement bias detection/mitigation, define safety metrics, contribute MLOps automation and safety tooling, and collaborate with engineers, researchers, and data scientists to improve scalable model safety and inclusivity.

Design and implement machine learning algorithms for robotic manipulation, focusing on RL and IL. Collaborate across disciplines to ensure integration and successful deployment.

Design and implement agent harnesses, evaluation benchmarks, and post-training pipelines. Develop RL environments and reward models, run and optimize multi-GPU training, profile and debug training jobs, and iterate on model fine-tuning to improve capabilities for agentic, edge-aware applications.

Design and build scalable, permission-aware retrieval and reasoning systems for Zoom's AI Companion. Implement vector and hybrid search, RAG pipelines, ranking and evaluation frameworks, indexing, and NLP/entity-extraction pipelines. Collaborate with product, infrastructure, and research teams to deploy production-grade, multi-tenant search and LLM-based applications.

Optimize and deploy LLM inference systems across multi-node clusters to maximize throughput and minimize latency. Implement and benchmark inference optimizations, profile GPU and memory bottlenecks, build evaluation and benchmarking frameworks, and collaborate with research scientists to integrate new model architectures and techniques into production.

As a ML Engineer focused on NLP at Avoma, you will improve the Insights Engine, develop new models with LLMs, maintain code quality, and stay updated on NLP advancements.

The Senior Machine Learning Engineer will design and build distributed systems for personalized content recommendations, ensuring deployment and optimization of ML platforms.

Lead end-to-end silicon diagnostic pipeline using AI/ML to analyze scan failures and process data, build models for failure classification and predictive yield, orchestrate AI-based EDA tools, and drive cross-functional root-cause analysis to improve yield and time-to-market.

Build and deploy production-grade AI/ML inference systems for a cloud security platform. Optimize LLM serving (vLLM, SGLang, KV cache), implement model evaluation/reporting, and collaborate with ML scientists and engineers to execute the AI roadmap.

Develop and deploy 2D traffic sign detection models for autonomous driving. Responsibilities include scenario analysis, dataset curation, model training and evaluation, optimization and quantization, building evaluation pipelines, long-tail data mining, and validating performance on edge/onboard platforms while collaborating across data, infrastructure, and deployment teams.

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

The AI Research Scientist will develop and evaluate generative models for scientific data, collaborate with teams to apply AI in materials science, and mentor junior members while staying current in the field.

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

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.