Top AI & Machine Learning Jobs

Develop and maintain full-stack applications, data analytics, visualization, and reporting solutions for engineering teams. Collaborate with customers and peers to analyze needs, resolve issues, and deliver functionality. Apply modern web, database, cloud, DevOps, CI/CD, testing, observability, and AI-assisted development technologies while emphasizing maintainability, user experience, system quality, and production supportability.

Develops predictive models and LLM-based AI solutions to optimize semiconductor manufacturing operations. Analyzes manufacturing data, identifies bottlenecks, builds dashboards, and deploys models in GCP. Collaborates with product, IT, and AI teams while applying MLOps, AutoML, model monitoring, and advanced machine learning techniques to improve fab efficiency and decision-making.

Architect and deploy autonomous Agentic AI systems for semiconductor manufacturing. Responsibilities include developing multi-step AI agents, integrating LLMs with enterprise tools, building RAG and multi-agent pipelines, evaluating and profiling agent performance, deploying workloads through CI/CD to Kubernetes and cloud environments, implementing security controls, and mentoring engineers. The role requires production-scale AI, data pipeline, inference optimization, and systems engineering expertise.

Develop and deploy full-stack AI applications for semiconductor manufacturing and autonomous operations. Responsibilities include building data pipelines, AI models, APIs, web interfaces, monitoring systems, and manufacturing integrations. The role uses Python, modern JavaScript, cloud platforms, containers, Kubernetes, CI/CD, generative AI, robotics, and manufacturing execution systems. Engineers also implement model evaluation, drift detection, responsible AI, security, audit logging, and governance practices.

Designs and deploys production-grade full-stack AI applications for autonomous semiconductor manufacturing. Responsibilities span data pipelines, model training and inference, APIs, web interfaces, digital twins, robotics integrations, LLMs, cloud and edge deployments, container orchestration, GPU optimization, monitoring, and Responsible AI practices.

Build and operate production-grade full-stack AI applications and custom agents using MCP, RAG, LLMs, and cloud-native technologies. Own features throughout design, implementation, testing, deployment, observability, and lifecycle support. Integrate AI services, APIs, databases, and Kubernetes platforms while ensuring reliability, security, scalability, and maintainability for critical manufacturing systems. Collaborate across engineering and business teams and use AI-assisted development practices to accelerate delivery.

Develop AI Agent prototypes and Python automation solutions to improve productivity in semiconductor photo engineering workflows. Responsibilities include analyzing engineering data, identifying automation opportunities, building data-processing pipelines, creating dashboards, evaluating solution effectiveness, documenting designs, and presenting technical findings. The intern will gain experience with generative AI, large language models, data analytics, and semiconductor manufacturing engineering.

Lead a data science team to deliver cost-optimization analytics and AI solutions for procurement and manufacturing. Own model development, production deployment, monitoring, and governance. Drive adoption of Generative AI, LLMs, and agentic systems to produce measurable cost savings and integrate solutions into business workflows while mentoring staff and managing hiring and resource allocation.

Design, develop, test, and support AI-enabled software applications using full-stack technologies, LLMs, AI agents, retrieval systems, APIs, databases, and cloud architectures. Collaborate with engineering and business teams, research emerging AI technologies, prototype solutions, and contribute to testing, documentation, automation, and continuous improvement.

Develop and deploy GenAI, agentic, machine learning, and analytics systems for ASIC semiconductor engineering workflows. Build LLM workflows using prompting, RAG, Graph-RAG, evaluation, and benchmarking; develop models for classification, anomaly detection, failure analysis, and decision support; and operationalize solutions in cloud and enterprise environments. Collaborate with engineering and technical teams, communicate findings, and lead initiatives that improve engineering productivity.