Machine Learning Jobs in San Jose, CA

The role involves leading compiler engineering, innovating compiler infrastructure and optimization algorithms for ML performance, and working with various teams to implement solutions.

Design, develop, and productionize scalable AI systems for autonomous threat detection and response. Build LLM-based reasoning engines, graph-based analytics, streaming feature pipelines, orchestration frameworks, and evaluation/reliability tooling. Collaborate with product, detection engineering, and customer teams to translate attacker behavior into robust ML and rule-based detections.

Develop and deploy lightweight machine learning models for edge AI, working on hardware integration and optimization techniques while mentoring junior engineers.

Lead design and implementation of cloud-native, microservices backend systems that power AI-enabled features (LLM assistants, RAG pipelines, recommendations). Build scalable REST and event-driven APIs, establish MLOps practices, ensure reliability/security/observability, mentor engineers, and collaborate with product, scientific, and infrastructure teams to productionize AI services in regulated scientific environments.

The Machine Learning Engineer will research and develop AI-driven solutions to analyze customer data, optimize ML models, and collaborate with cross-functional teams.

Lead the design, development, and implementation of advanced machine learning models, collaborating with teams to enhance services through AI/ML solutions.

The role involves applying machine learning techniques to circuit design, analyzing datasets, building and optimizing models, and collaborating with teams on various projects in VLSI and circuit design.

Fine-tune and optimize LLMs (including multi-modal) for humanoid robots and autonomous vehicles; build scalable training and deployment pipelines; work on model efficiency (quantization, weight sharing), synthetic data generation, and cross-team product alignment.

Develop and maintain machine learning models for robotics applications, collaborate across teams, and contribute to innovative solutions in AI and mobility.

The role involves designing and implementing machine learning systems for knowledge representation and temporal reasoning, focusing on organizational intelligence. Responsibilities include building LLM-powered pipelines, developing memory algorithms, and researching various ML techniques.

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

Seeking a Machine Learning Engineer to build models from idea to delivery, maintain production models, and develop system architecture while collaborating on data-driven strategies.

Design and implement machine learning models, optimize algorithms, deploy solutions, and collaborate with teams to enhance services through AI/ML solutions.

Lead the design, development, and implementation of machine learning models to solve complex problems, collaborating with cross-functional teams to enhance services and deploy solutions in production environments.

The job involves developing and optimizing machine learning models, overseeing model risk, and collaborating with teams on AI-related projects.

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

Lead the development of RL training frameworks and data processing pipelines for AV models, ensuring quality and scaling training workflows.

Architect, build, and scale high-performance ML platform infrastructure using IaC (Ansible, Terraform). Apply SRE practices to ensure reliability across multi-cloud and on-prem GPU clusters, develop automation and orchestration tooling, operate Kubernetes/Docker workloads, participate in on-call rotation, and collaborate with researchers to support end-to-end ML workflows.

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

The Principal Engineer for AI/ML at McAfee leads the design and deployment of scalable AI solutions, mentors engineers, and drives technical strategies in a hybrid work environment.

Develop end-to-end perception solutions for autonomous driving, leveraging deep learning and data-driven approaches to enhance system accuracy and efficiency.

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.

As a Sales Executive, you will hunt for new clients, manage enterprise accounts, and create revenue streams while collaborating with internal teams.

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

As a Staff Machine Learning Engineer, you'll build production-scale ML models for personalization and risk management, mentor junior scientists, and collaborate with cross-functional teams to drive impactful product features.