Machine Learning Jobs in Santa Clara, CA

Define long-term architecture and technical strategy for Target's enterprise ML/Ops platform. Design cloud-native, scalable, secure Kubernetes-based platforms; establish ML lifecycle, deployment, governance, observability, and responsible AI standards; evaluate technologies; mentor engineers; and partner with infrastructure, security, and product teams to modernize platform capabilities.

The role involves developing privacy technology in machine learning, collaborating on modeling, and creating solutions for TikTok's product privacy requirements.

Design and build machine learning algorithms to optimize e-commerce search and improve overall platform matching capabilities, supporting TikTok's rapid e-commerce development.

Develop and optimize Python and C++ software to compress, convert, deploy, and run neural networks on analog compute-in-memory hardware. Implement quantization, pruning, graph optimizations, runtime APIs, and profiling tools. Collaborate with researchers and hardware engineers, perform testing, debugging, and CI, and support research, documentation, and validation of hardware-software co-design for edge and embedded AI systems.

As a Machine Learning Engineer, you will develop models for NLP and code-generation systems, analyze data, and improve AI performance using advanced algorithms. You'll manage the entire ML lifecycle and ensure effective communication of model benefits across the organization.

Lead the development and deployment of advanced machine learning models focusing on NLP, while ensuring model integrity and communication with stakeholders.

Develop and deploy machine learning algorithms for planning in autonomous trucks, ensuring safety and real-time efficiency.

As an MLOps engineer, you will develop optimized data pipelines, enhance data engine architecture, and integrate foundation models while working closely with DoD customers.

Own and optimize the AI inference and execution layer: quantization, KV-cache, batching, latency/memory tuning, fine-tune and evaluate models, build bounded task runtimes, enable hardware acceleration and partner with systems/backend teams to ship production-grade agentic AI capabilities.

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

Define and execute the scientific roadmap for search and recommendation: architect large-scale ranking and retrieval systems, develop and distill Ranking Foundation Models, integrate vector/RAG/hybrid retrieval into production, partner with product and engineering, and lead and scale multidisciplinary ML teams to optimize marketplace health.

Design and oversee the development of robust data platforms for training and evaluating ML models, focusing on autonomous driving. Collaborate with multiple teams and optimize data processing pipelines.

As a Machine Learning Engineer/Scientist, you will develop ML systems for cryopreservation research, manage data pipelines, and deploy models for scientific insight.

Design, build, and operate large-scale ML systems for ad performance: feature engineering, model training/deployment (batch and real-time), evaluation, monitoring, and production optimization while collaborating with cross-functional teams.

Lead the Inference ML team in developing tools and APIs for large-scale ML applications, enhancing performance and usability, while collaborating across engineering teams.

The AI/ML Engineer designs and operates a production-grade orchestration platform, focusing on multi-agent workflows, LLM integration, and backend systems. Responsibilities include managing cloud-native infrastructure, ensuring system reliability and observability.

Lead design, build, deploy, and maintain production-grade AI/ML systems (LLMs, agentic architectures, classical ML). Provide hands-on technical leadership across architecture, model selection, implementation, testing, deployment, observability, and operational support while collaborating with data scientists, platform and security teams and mentoring engineers.

The role involves deploying and optimizing LLMs, designing the ML serving stack, and ensuring high-performance GPU services for production readiness.

As a Staff Machine Learning Engineer, you will lead the development and deployment of AI models for autonomous driving, focusing on safety and reliability. Responsibilities include driving the roadmap, training models, and collaborating across teams.

Lead design, training, and deployment of large-scale ML models (LLMs, diffusion, GNNs). Own end-to-end training pipelines, distributed training, evaluation, and inference architecture. Collaborate with data and infra teams, drive reproducible model development, and provide technical leadership and mentorship from research through production.

As a Machine Learning Engineer Intern, you'll bridge the sim-to-real gap in data for autonomous truck software, optimizing and integrating BEV features and supporting self-play reinforcement learning.

Join PlusAI as a Machine Learning Engineer Intern to develop AI-based features for autonomous trucks, focusing on multi-modal data processing and event classification.

As an intern, you will research and implement algorithms in computer vision and machine learning, developing new features for Sony's digital imaging technology.

The Physical Design Engineer will lead efforts in solving physical design challenges, improve RTL-to-GDS methodologies, and integrate AI/ML solutions into CAD flows, focusing on performance and runtime improvements.

The Senior Machine Learning Engineer will develop and optimize AI image generation models, monitor production issues, and collaborate on ML solutions for generative features.