Top AI & Machine Learning Jobs

Own the AI roadmap for battery manufacturing, identifying, benchmarking, piloting, scaling, and retiring AI applications tied to yield, quality, cost, and launch speed. Build proofs of concept and reference architectures for battery formation, aging, coating defects, yield genealogy, and predictive maintenance. Establish reusable data, labeling, and model standards; evaluate vendors; guide embedded engineers; and align AI solutions with IT, OT, security, and plant-scale deployment.

Develop and maintain predictive models and optimization frameworks for vehicle pricing, incentives, portfolio risk, and profitability. Own analytical products from design through production deployment and maintenance, using statistical modeling, machine learning, and optimization. Partner with business, data engineering, and AI/ML teams to translate ambiguous problems into quantitative solutions, communicate insights, and deliver web applications, dashboards, and rapid-response analyses.

Lead Ford’s data mesh strategy and architecture across manufacturing and enterprise domains. Develop governed data products, data models, semantic layers, knowledge graphs, and batch or streaming pipelines using GCP technologies. Establish data quality, security, lineage, interoperability, and observability standards while partnering with analytics, product, cybersecurity, legal, and compliance teams. The role also involves mentoring technical teams and modernizing data workflows with AI and cloud-native patterns.

Lead strategy and execution for a mobile consumer AI assistant, improving task completion, response quality, adoption, engagement, and retention. Use analytics, experimentation, research, and AI evaluations to prioritize improvements. Partner with engineering, AI/ML, design, safety, and marketing to enhance grounding, latency, reliability, and safety while driving cross-functional initiatives and communicating results to senior leadership.

Designs, deploys, and optimizes machine learning solutions for large-scale data platforms, including identity resolution, anomaly detection, data quality, predictive modeling, and Generative AI applications. Leads data science research and platform improvements, manages ingestion and transformation into BigQuery, analyzes pipeline performance, and performs root-cause investigations. Partners with engineers, architects, privacy, product, and global stakeholders while mentoring team members and translating business needs into technical specifications.

Leads the architecture, development, deployment, and scaling of enterprise AI solutions, including generative AI, copilots, agentic systems, RAG, conversational interfaces, and intelligent automation. Establishes AI engineering, MLOps, LLMOps, governance, monitoring, and responsible AI practices. Partners with product, engineering, analytics, and business teams to deliver measurable outcomes, builds reusable frameworks and accelerators, and mentors AI engineers while driving enterprise AI adoption.

Build and deploy full-stack, LLM-powered analytics applications and RAG/multi-agent pipelines. Own GCP infrastructure using Terraform, implement CI/CD and DevOps, design system architecture, collaborate with product teams, and drive reliability and continuous improvement.

Design, build, and operate AI-powered full-stack applications and agentic workflows on GCP. Own architecture, IaC, CI/CD, integrations, databases, and developer productivity tooling while automating repetitive work and scaling RAG/LLM systems for production.

Design, build, and operate AI-powered full-stack applications and agentic workflows on GCP. Own architecture, IaC, CI/CD, integrations with enterprise SaaS, databases, and production-grade RAG/LLM pipelines. Automate developer workflows and ship scalable, secure systems.

Design, build, and operate Ford's cloud-native platform on GCP. Automate infrastructure and CI/CD, orchestrate AI-assisted agents, run and scale LLM/agent workloads, implement observability and SLO-driven reliability, and embed security and compliance into platform pipelines to enable fast, reliable delivery for product teams.