Top Artificial Intelligence Jobs

Associate Corporate Counsel will advise insurance business leaders on regulatory and corporate legal matters, privacy, AI governance, compliance, commercial agreements, governance, and enterprise risk. The role supports regulatory inquiries, examinations, filings, market conduct matters, legislative monitoring, and compliant insurance products while partnering with executive and cross-functional teams. The position requires a California-licensed attorney with substantial insurance law and regulatory experience, strong business judgment, and the ability to translate complex legal issues into practical guidance.

The Staff Research Scientist will conduct AI research, focusing on embedding models and LLMs while collaborating with other scientists and engineers on practical deployments and innovations in information retrieval.

Own product strategy and execution alongside founders, leading growth engineering pods and managing roadmaps, priorities, launches, and iterations. Conduct customer research, analyze usage data, define metrics, improve UX, and raise product quality standards. The role requires 0-to-1 product development experience, strong product sense, technical fluency, comfort with ambiguity, and excellent communication. Experience with AI products, automation, sales technology, or early-stage startups is preferred.

Leads business architecture and customer experience initiatives that connect enterprise strategy to execution. Assesses capabilities, analyzes solutions, prioritizes investments, develops roadmaps, maps customer journeys, and oversees business outcomes. Partners with executives and cross-functional teams, leads design and process-improvement efforts, facilitates workshops, coaches stakeholders, and evaluates emerging technologies including AI and automation. Supports projects as a subject matter expert or proxy product owner while driving customer-centric culture and strategic alignment.

Lead product strategy for enterprise workflows across Finance, HR, Legal, and global operations. Drive adoption of agentic AI, workflow automation, and intelligent orchestration to create autonomous enterprise functions. Partner with senior business and technology leaders to prioritize investments, standardize platforms and processes, establish governance, and deliver secure, scalable solutions with measurable outcomes across global markets.

Senior Business Architect responsible for connecting enterprise strategy to execution through business architecture, capability assessments, solution analysis, investment prioritization, roadmapping, and customer journey mapping. The role leads experience improvement initiatives, prototypes emerging technologies, supports projects as a subject matter expert or proxy product owner, and coaches teams in customer-centric methods. It requires cross-functional leadership, stakeholder influence, business process optimization, architecture modeling, and alignment of technology and business strategies.

Drive AI/ML initiatives across storage product development, coordinating engineering, firmware, validation, and data science teams from requirements through deployment. Manage schedules, dependencies, blockers, readiness reviews, portfolio progress, and Go/No-Go decisions. Align priorities with business goals, identify AI/ML opportunities, support adoption of AI-assisted tools, and communicate program status and outcomes to executives and cross-functional stakeholders.

Develop GenAI, machine learning, deep learning, and analytics solutions for ASIC semiconductor engineering workflows. Build LLM and agentic systems using prompting, RAG, Graph-RAG, benchmarking, and evaluation. Deploy and monitor production AI/ML solutions in cloud and enterprise environments, collaborate with architecture, design, data science, and IT teams, and communicate technical insights to diverse stakeholders.

Develop and productionize GenAI, LLM, machine learning, and analytics solutions for ASIC semiconductor engineering. Build agentic systems, RAG workflows, predictive models, and failure-analysis tools; deploy and monitor AI solutions in cloud and enterprise environments; collaborate with architecture, design, data science, and IT teams; and communicate technical findings while identifying opportunities to improve engineering productivity, quality, cost, and cycle time.

Own an AI-native product spanning AI Specialists and Voice AI from customer problem definition through launch and production performance. Deploy solutions within enterprise environments, analyze agent execution logs and evaluation data, define quality standards, establish outcome metrics, lead design partnerships, translate customer insights into roadmap improvements, and coach product managers. The role requires shipping enterprise LLM products, designing evaluations, building prototypes, and generalizing forward-deployed solutions across customers.

The Principal Engineer will lead the design and delivery of core platform capabilities, focusing on scalable systems, AI application, and mentoring engineers while driving architectural decisions and improving operational systems.

Leads the integration and adoption of modeling, simulation, and AI capabilities across clinical trial design workflows. Maps processes, identifies automation opportunities, translates business needs into technical requirements, develops fit-for-purpose processes and governance, trains users, monitors adoption, and improves workflows in collaboration with statisticians, data scientists, developers, and therapeutic-area stakeholders.

Leads cybersecurity data science initiatives, including exploratory analysis, statistical modeling, machine learning, deep learning, feature engineering, model governance, and secure production coding. Partners with cybersecurity, engineering, and business stakeholders to define use cases, deploy reliable models, validate AI-assisted recommendations, establish AI workflow guardrails, and address security, resiliency, auditability, and regulatory requirements.

Leads the company’s digital transformation strategy, roadmap, and enterprise platforms across engineering, manufacturing, supply chain, quality, and finance. Owns PLM, ERP, MES, integrations, data, automation, and emerging technologies while establishing an integrated digital thread, scalable architecture, data governance, and system-of-record strategies. Directs complex transformation initiatives, process redesign, automation, AI adoption, vendor partnerships, governance, and technical teams through implementation and measurable business outcomes.

Lead the design, development, deployment, and support of scalable AI platform components, including foundation model training, LLM inference, similarity search, guardrails, evaluation, governance, experimentation, and observability. Optimize AI training and inference for latency, throughput, cost, and hardware utilization. Partner with engineering, research, product, and program teams while contributing to technical strategy and mentoring stakeholders.

Lead the design, development, deployment, and support of foundational AI systems, including LLM inference, model training, similarity search, guardrails, evaluation, governance, and observability. Drive build-versus-buy decisions, optimize production AI systems for scalability, cost, latency, and throughput, and shape long-term technical roadmaps. Manage, mentor, and grow an engineering team while partnering with scientists, product managers, and technical program managers.

Build and support production AI platform components including foundation model training, LLM inference, similarity search, guardrails, evaluation, governance, experimentation, and observability. Optimize large-scale AI systems for scalability, latency, throughput, cost, and hardware utilization. Partner with engineering, research, product, and program teams; contribute to technical vision and roadmap; and lead or mentor engineering teams.

Conduct applied AI research and build foundation models across design, training, evaluation, validation, and deployment. Collaborate with data scientists, engineers, and product managers to deliver scalable AI-powered products. Research state-of-the-art methods in language and vision-language modeling, optimize training and inference, develop platform or solution code, and translate research findings into customer-focused business outcomes.

Lead development of responsible, scalable AI systems, including foundation-model training, LLM inference, similarity search, guardrails, evaluation, governance, experimentation, and observability. Optimize production AI systems for scalability, latency, throughput, hardware utilization, and cost. Partner with engineering, research, program management, and product teams while shaping the technical vision and roadmap for foundational AI platforms.

Design, develop, and deploy AI-powered healthcare solutions using Java and Spring Boot microservices. Implement HL7 FHIR and Da Vinci interoperability standards across prior authorization, claims, eligibility, and provider domains. Build Kafka-based event-driven services, integrate relational databases, automate CI/CD, deploy with Docker and Kubernetes on AWS or Azure, and establish monitoring using Splunk, ELK, Prometheus, and Grafana.

Owns the full partnership lifecycle for strategic Data and AI partners. The role develops joint agentic solutions, supports complex enterprise sales cycles, leads AI value workshops, manages OEM account adoption, negotiates commercial frameworks, informs the AI product roadmap, and validates LLM integrations and zero-copy data strategies with engineering teams.

Leads AMPP’s global standards portfolio and Technical Engagement functions, overseeing standards quality, modernization, adoption, revenue growth, volunteer participation, and international relevance. Directs standards operations, technical teams, budgets, performance metrics, cross-functional initiatives, and stakeholder relationships. Develops portfolio strategy, improves standards processes, advances machine-readable formats, and represents AMPP with industry, government, academic, and standards organizations. Requires significant senior leadership experience in standards development, technical operations, association management, or related fields, with substantial travel.

Designs and implements data warehousing, analytics, dashboards, semantic layers, and reporting solutions for supply chain planning operations. Collaborates with business, supply chain, and IT stakeholders to define requirements, document workflows, develop user stories, validate data, and improve SAP data accessibility through Databricks. Maintains data definitions and governance, tests data assets, ensures reporting accuracy, and supports self-service analytics. Requires deep supply chain experience, BI development expertise, and strong SQL, Python, and data modeling skills.

Support consulting engagements focused on AI transformation and digital strategy. Responsibilities include leading workstreams, researching markets and technologies, structuring ambiguous business problems, developing operating models and implementation roadmaps, translating technical concepts for executives, collaborating with data and technology teams, facilitating client workshops, presenting recommendations, mentoring junior staff, and strengthening client relationships.

Develop AI and machine learning analytics products for Ford’s cost efficiency analytics team. Responsibilities include designing and optimizing deep learning and LLM models, deploying models to production, building AI infrastructure and data pipelines, evaluating model performance, and collaborating with data, software, and product teams. The role translates business needs into analytical solutions, supports Google Cloud Platform delivery, and advises business stakeholders on analytics strategy and value.