Top AI & Machine Learning Jobs

Define and lead AI architecture to enable scalable capabilities, design complex workflows, and build modern data infrastructure, driving enterprise AI adoption.

Lead Solana Foundation's Bay Area strategy to drive AI × Solana adoption. Build relationships with AI/ML founders, VCs, and researchers; allocate capital and grants; launch flagship events; form strategic partnerships; and convert partnerships into shipped products. Must be Bay Area–based and accountable for regional outcomes.

Lead enterprise data strategy and design cloud-native lakehouse platforms. Build real-time and batch pipelines, architect generative AI (RAG, vector DBs, multi-agent), implement MLOps and feature stores, create BI solutions, establish governance, and lead/mentor technical teams while consulting with executive stakeholders.

Early-career ML engineer builds data pipelines and trains scientific ML models (surrogate and physics-informed), develops visualization/dashboard tools, runs experiments, and hardens prototypes for production while collaborating across engineering teams.

Lead Core AI to build models, agents, and training systems that enable AI-driven physics discovery. Set roadmap, own research portfolio and infrastructure, hire and coach top talent, stay technical as a player-coach, and ensure results are scientifically trustworthy and production-ready for Applied AI and Engineering teams.

Build an end-to-end content engine for growth: ingest sources, extract themes, draft content, run evals, and manage review. Create webinars and videos, measure quality and engagement, own pipeline metrics, and iterate with founders and customers. Implement multi-step LLM pipelines, prompt engineering, and agentic workflows to produce high-quality, author-voiced content.

Lead individual-contributor data science work to build predictive models, measurement frameworks, and AI-powered analytics. Partner with Product, Engineering, and Operations to translate insights into product strategy, define success metrics, automate analyses with agentic workflows, apply causal inference and experimentation, communicate findings to executives, and mentor other data scientists.

Lead end-to-end engineering for production AI agents and platform systems. Own architecture, deployment, scalability, security, and reliability. Use agentic coding to accelerate development, mentor teammates, and collaborate with data scientists and customers to deliver production-grade AI workflows.

The Staff ML Engineer will lead the architecture and development of AI/ML features, oversee complex systems, mentor teams, and enhance product experiences through ML solutions.

Lead product strategy, roadmap, and delivery for Risk & Quality AI solutions. Operationalize LLM/NLP/OCR/agent workflows, define evaluation and monitoring, ensure responsible AI and regulatory compliance, partner with engineering/ML/clinical/compliance, and mentor a product team to deliver measurable client impact.