Artificial Intelligence Jobs in San Jose, CA

Research and implement methods to transfer human-object interaction data into robotic dexterous manipulation policies. Build large-scale data pipelines, generative embodied-AI models, unified cross-embodiment training, and sim-to-real deployment; validate on real multi-fingered or humanoid hardware and produce a technical manuscript or demo.

Design and implement high-performance, scalable inference systems for LLMs and VLMs across cloud, edge, and hybrid platforms. Develop and optimize custom kernels for hardware accelerators (GPU/NPU/DSP), integrate tensor/model parallelism, caching, and quantization, and collaborate with system and hardware teams to deploy production-grade AI inference.

The Technical Product Manager will lead product direction, interface with engineering teams, and ensure the successful deployment of AI-powered safety systems. Responsibilities include defining product vision, translating customer needs, and maintaining high product quality.

Build and prototype state-of-the-art large language model systems and deep AI assistants, take services from ideation to production, fine-tune and compare open-source and commercial LLMs, use cloud platforms, analyze model and projection accuracy, and collaborate cross-functionally.

Provide embedded legal counsel to product and engineering on privacy, IP, and consumer protection; lead Arena's global privacy program (GDPR, CCPA/CPRA, cross-border transfers); advise on AI governance and responsible deployment; support commercial contracts and data flow frameworks; build privacy processes, playbooks, internal agents, and legal-tech integrations to scale operations.

Develop and implement compiler optimization algorithms for deep learning workloads, analyze networks, debug and profile GPU performance (CUDA), define public APIs, and deliver ahead-of-time and just-in-time compilation techniques for NVIDIA GPUs to improve inference performance, build times, and memory efficiency.

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.

Design, build, and operate internal IT platforms to improve availability, scalability, and efficiency. Automate incident response, capacity planning, performance tuning, and integrations across enterprise applications. Collaborate with engineering and internal teams to define architectures, standards, and documentation for large-scale distributed systems. Support on-call, alerting, and reliability practices.

Founding technical role to design, build, and deploy internal AI tools and agents using LLMs and retrieval systems. Partner with investors and the data team to accelerate sourcing, diligence, portfolio insights, and define CRV's AI strategy through rapid prototyping and production-grade systems.

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.

Lead robotics training data strategy: define formats and quality standards, oversee data collection/annotation, partner with researchers and external providers, build data acquisition plans, and deliver large-scale datasets and tooling to support robotics foundation models.

Design, build, and scale the AI platform for distributed multi-GPU training, experiment tracking, and model deployment. Optimize GPU clusters, networking (NCCL/InfiniBand), Kubernetes-native scheduling, and inference serving (TensorRT, Triton). Develop agentic automation for cluster health, CI/CD, and data curation while integrating ML lifecycle tooling and observability.

Design, implement, and operate internal IT systems and platforms to improve availability, scalability, and efficiency. Automate responses to recurring issues, collaborate with engineering teams on architecture and standards, perform capacity planning, performance analysis, and system tuning, and produce documentation for systems ownership.

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.

Design and build native Android applications for robot data annotation, dataset review, and experiment tracking. Create fast, reliable mobile experiences for real-time data capture, integrate AI models to automate annotation, and collaborate with AI researchers, robot operators, and web tooling teams to ensure consistent workflows.

The Data Infrastructure Engineer will design, build, and maintain data tools for robot data management, supporting AI researchers and ensuring optimal data solutions across storage and compute resources.

The Director of AI will lead AI and data engineering teams, define and execute AI strategy, and ensure effective partnership across functions, focusing on scalable AI solutions and responsible AI practices.

Design and build scalable EDA flows and AI/LLM-driven automation for 3DIC, interposer, and RDL systems. Implement flow-generation, debugging assistants, root-cause analysis, and platform integration using C++, Tcl, Python, and Java. Collaborate with engineering teams to validate flows, automate constraint-driven designs, and address large-scale, high bump-count and multi-region routing challenges to deliver production-quality solutions.

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.

Lead AI feature design and delivery, build large-scale ML systems, mentor engineers, and define best practices across the engineering organization.

Design, build, and optimize distributed PyTorch training systems for multi-node GPU clusters. Profile and eliminate performance bottlenecks across data pipelines to GPU kernels, implement low-level CUDA/cuDNN/Triton kernels, tune CPU/GPU/memory/network utilization, and develop monitoring and debugging tools for large-scale training runs.

Build, train, evaluate, and deploy production computer vision models (detection, segmentation, OCR, barcode decoding, visual search). Own dataset quality, tooling, model releases, inference exports, and monitoring. Collaborate cross-functionally to improve model performance, robustness, and deployment for retail robotics and edge environments.

As a Senior Full-Stack Software Engineer, you will design, build, and ship AI-powered systems to optimize workflows in the single family rental market, focusing on backend services and full-stack development in a fast-paced environment.

The Founding AI Engineer will design and build an AI-native compliance platform, handling complex financial data and developing scalable backend services, while establishing processes for enterprise readiness.

The intern will design and implement AI-driven automation workflows to enhance the silicon testing process, collaborate with engineers, and document automation recipes.