Artificial Intelligence Jobs in Fremont, CA

Lead the design and implementation of Microsoft Dynamics ERP solutions, manage large-scale projects, and innovate processes to enhance operational efficiency and client outcomes.

The AI Engineer Manager leads the Data and Analytics Engineering team in developing AI solutions, manages client accounts, analyzes data, and fosters team leadership and relationships with clients.

Lead US TMT business development with an AI focus: identify revenue opportunities from AI regulation, M&A, governance and data trends; develop client strategies, communications and targeted outreach; support pitches and major US TMT projects; coach and manage MBD colleagues; collaborate with partners and international teams to drive client relationships and revenue growth.

Senior technical leader responsible for architecting production-grade AI infrastructure for media & entertainment partners. Translate business needs into scalable GPU, storage, networking and inference solutions; validate ISV integrations; influence product roadmap; engage CTO-level stakeholders; and grow a team as the vertical scales.

The Staff Backend Engineer is responsible for designing and developing AI platforms, backend services, and APIs, ensuring scalability and reliability while collaborating with cross-functional teams and mentoring engineers.

As a Founding Account Executive, you will drive the sales cycle, generate leads, become a product expert, and expand customer accounts, all while serving as a trusted advisor on voice AI solutions.

The Senior Applied AI Engineer will build AI-driven interactive product demos, collaborating with design and engineering teams to create intelligent multimodal experiences.

Lead strategy and development of AI and technology products, managing cross-functional teams to enhance legal workflows and compliance standards.

Provide technical leadership for physical AI, virtual commissioning, and digital twin robotics applications. Support pre- and post-sales, perform feasibility studies, simulations, troubleshooting, root-cause analysis, and proof-of-concepts. Develop applications, improve robotics software, support vision/iRVision, and collaborate with sales and engineering teams to integrate and optimize FANUC robot solutions.

The Sales Strategy and Operations Manager leads complex sales opportunities, develops ROI models, engages executives, and coordinates cross-functional input for compelling proposals.

As an Applied AI Engineer at Luminai, you will design experiments, optimize machine learning models, and enhance AI platforms for healthcare automation.

Lead AI initiatives, establish strategy, and develop generative AI applications. Collaborate with teams to build and optimize financial products.

As a Backend Engineer at Brain Co., you will design, build, and operate backend services and data pipelines for AI products, focusing on robustness and scalability. You'll collaborate across teams and own the full service lifecycle while ensuring high availability and performance. You should have 3+ years of experience in production backend systems and a strong foundation in APIs and distributed systems.

As an AI Product Engineer, you will develop and deploy AI-driven software solutions, collaborate with customers, and optimize applications for real-world impacts.

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.

Build production-ready backend components that interface directly with LLMs, create tooling to automate LLM-in-the-loop testing, design and validate LLM-based solutions using real biomedical data, and develop metrics to measure model performance and reliability under production load.

The AI Engineer will design and build AI systems using LLMs, develop agent workflows, implement APIs, and collaborate with teams to optimize AI applications.

Develop and train large-scale multimodal foundation models (vision and language) that fuse imaging with molecular data, build scalable distributed ML pipelines for petabyte-scale datasets, and collaborate with experimental scientists and engineers to productionize models and systems.

Develop and productionize agentic engineering systems for a physical-world AI agent. Build and maintain the agentic harness and tool integrations, run evaluations, analyze production traces, debug failures, collaborate with research to create and deploy custom models, and incorporate new agentic frameworks into the product.

Lead applied research on spatial reasoning, geometric and physical world modeling. Own data generation, evaluation, and product integration. Implement experiments, build production code, and drive research strategy in 3D, multimodal, simulation, and related domains.

Lead applied research on continual learning, fast adaptation, memory, and transfer learning for agentic systems. Own end-to-end pipeline from data generation to evaluation and product integration, run experiments, produce code, and drive research strategy while mentoring and managing a small team.

Lead end-to-end delivery of enterprise AI engagements: run discovery, size value, define features, coordinate cross-functional teams, drive adoption and change management, and own commercial expansion and program outcomes in regulated environments.

Lead development and scaling of a customer-facing AI agents platform using LLMs and internal APIs. Define roadmap, prompt strategies, evaluation frameworks, guardrails, and experiments to improve automation, accuracy, latency, and customer satisfaction. Partner with engineering and OpenAI to deploy voice and workflow automation capabilities.

Lead development and scaling of a customer-facing Agentic AI platform using LLMs and agents. Define roadmap, prompt strategies, evaluation frameworks, and guardrails. Partner with engineering and OpenAI, run experiments to improve accuracy, latency, cost, and automation rates, and translate technical work across the organization.

Lead and own the AI platform strategy: consolidate prompts across systems, prioritize chatbot work, drive architectural decisions, manage OpenAI/ChatGPT integrations, and translate technical tradeoffs into execution while learning prompt workflows and coordinating engineering, data, and operations teams.