Artificial Intelligence Jobs in Sunnyvale, CA

Design and implement AI-native frameworks, optimize performance for deep learning models, and ensure high-quality software development using modern C++ and AI tools.

This role involves designing and exporting AI frameworks, enhancing product capabilities, collaborating with teams, and directly engaging with customers.

Responsible for productizing software stack for AI compute engine; focusing on development, optimization, and scaling of AI deployment software, collaborating with hardware and software teams.

As a Senior Staff Software Engineer, you will develop and maintain AI deployment software, optimizing hardware-software co-design, and work closely with various experts to enhance infrastructure.

Responsible for defining and validating SoC IP blocks, collaborating with teams to ensure quality silicon for AI applications, and mentoring junior engineers.

Develop and maintain automated regression test suites (Python, JavaScript), integrate tests into CI/CD, optimize test cycles for firmware and receiver interaction, collaborate cross-functionally, and present frontend test results to stakeholders to ensure high-precision GNSS positioning reliability.

Design, build, and operate AWS-native AI/ML systems: RAG pipelines, fine-tuned LLMs, and microservices. Implement IaC (Terraform), CI/CD, policy guardrails, and production deployments integrating Bedrock/SageMaker, LangChain, and telemetry for secure, scalable enterprise use.

Lead RTL development and microarchitecture of an AI-enabled RISC-V CPU core, including vector and matrix engines. Drive design for performance, power, area, verification support, and SoC integration while collaborating with cross-functional teams to meet timing, reliability, testability, and power goals.

Lead cross-functional initiatives to increase engineering productivity via AI-assisted development tools, workflows, and dashboards. Drive adoption, define productivity metrics, enforce governance and cost controls, coordinate automation (test generation, incident workflows), run enablement programs, and report executive-level impact and roadmap progress.

Design, train, evaluate, and optimize agent-native LLMs and RAG pipelines for enterprise use. Build training and RL pipelines (RLHF/DPO/PPO), embedding-based memory, evaluation harnesses, observability, and inference optimization across cloud and on-prem environments.

Build and operate secure, scalable ML pipelines and hybrid GPU infrastructure for training, fine-tuning, serving, and governing open-source LLMs and agentic apps. Implement CI/CD, model versioning, observability, benchmarking, security controls, and integration with RAG/agent tools and vector DBs to support production deployments across tenants.

Design behavioral specs, harnesses, and validation systems so AI agents implement reliable full-stack features. Build CI gates, structural tests, linting, knowledge bases, and end-to-end verification. Diagnose failures across spec, harness, and agent layers. Architect and operate production systems across cloud and on-prem environments for regulated energy deployments, prioritizing reliability, observability, and graceful degradation.

Manage strategy creation, sales account management, and relationship building for Akamai. Drive revenue growth and improve customer relations in the telecommunications sector.

Build and scale core AI-native healthcare products across the stack, focusing on real-time audio processing, NLU/transcription, clinical documentation, EHR integrations, and distributed production systems. Drive architecture, ship features with high ownership, and operate in a fast-moving, regulated environment.

Design, build, and deploy reliable, outcome-driven AI systems and agentic workflows that plan, reason, and act across enterprise systems. Integrate LLMs into production, work with structured and unstructured data to ground models in business context, and partner with product to ship end-to-end capabilities.

Seeking Applied AI Research Scientists to develop and explore new AI approaches, algorithms, and methodologies to drive AI systems across S32 portfolio companies and shape company direction.

S32 seeks Applied AI Research Scientists to explore new approaches, algorithms, and methodologies to advance AI systems across portfolio companies. Candidates will drive research, shape company direction, and collaborate with founders to apply research outcomes to product and business needs.

Audit and ensure quality of data labeling work for ML using human-in-the-loop processes. Provide feedback, train and support annotators, monitor workflows, identify recurring issues, and help improve guidelines and processes.

Evaluate AI outputs, perform audit-based reviews, identify patterns and failure modes, apply and improve annotation/evaluation guidelines, document findings, and communicate insights to cross-functional teams to improve model performance and evaluation frameworks.

Lead and manage a team of Chinese (zh-CN) multimodal annotators and reviewers to meet throughput and quality targets. Own guidelines, calibration, audits, reporting, onboarding, and cross-stakeholder coordination for multimedia labeling workflows.

Evaluate AI model outputs, perform audit-based reviews to identify patterns and failure modes, apply and refine guidelines, document findings, and collaborate with leads and cross-functional teams to improve model evaluation and data quality.

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