Top AI & Machine Learning Jobs

Build and operate supply chain data pipelines, models, integrations, quality controls, and dashboards supporting procurement, inventory, logistics, suppliers, and project analytics. Partner with analysts and business teams to enable forecasting, planning, scorecards, KPI tracking, and data governance. Contribute to cloud data architecture, engineering standards, tooling implementations, and productionizing analytics models across AI infrastructure and capital-intensive supply chain operations.

Supports oncology research through clinical, genomic, sociodemographic, behavioral, wearable, and patient-reported data processing and analysis. Assists with data cleaning, harmonization, integration, visualization, AI risk algorithms, dashboards, digital health tools, and supportive care applications. Participates in supervised research, data pipeline reviews, troubleshooting, and mentor meetings.

Build and operate full-stack features for an AI-powered consulting platform. Responsibilities include frontend and backend development, data pipelines, APIs, LLM-based extraction, retrieval, chat, content generation, AI coding workflows, testing, evaluations, and quality checks. The role owns platform areas end to end, collaborates with consultants, product, and design, and translates expert judgment into reliable AI-assisted functionality.

Build and ship production AI systems for enterprise portfolio and planning workflows. Develop anomaly and signal detection, LLM-powered insight synthesis, natural-language planning rule translation, evaluation frameworks, and MCP tools. Partner with external AI providers initially, then establish internal standards for prompt design, model selection, agent architecture, reliability, and evaluation. Collaborate closely with product, design, and domain engineering teams.

Leads global engineering teams developing Private AI products and services, integrating with vSphere, NSX, and vSAN. Owns platform reliability, security, lifecycle management, SDLC execution, and day-two operations. Defines Broadcom’s upstream contribution strategy and represents the company in the Kubernetes and CNCF ecosystems. Requires deep Kubernetes expertise, significant open-source influence, distributed-systems experience, and global engineering leadership.

Lead AI/ML initiatives for Air Force condition-based maintenance, from predictive model development through production deployment. Architect data pipelines, develop machine learning, deep learning, NLP, and generative AI solutions, and build knowledge graph capabilities. Provide technical leadership, integrate models into applications and workflows, and monitor data quality, model performance, drift, and bias. Collaborate with engineering, cloud, software, and product teams while supporting defense aviation logistics and sustainment missions.

Enables business teams to adopt governed AI by identifying use cases, demonstrating and prototyping AI assistants and automations, training users, testing outputs, and guiding risk reviews. Creates enablement materials, tracks adoption and business outcomes, supports AI requirements development, and collaborates with IT, Security, Legal, and AI governance stakeholders. The role emphasizes responsible AI, process improvement, stakeholder engagement, and practical adoption of approved generative AI tools.

Develops advanced analytics, machine-learning, and AI solutions for quality monitoring, risk identification, root-cause analysis, and performance improvement. Builds and deploys models, dashboards, datasets, and automated workflows using statistical, predictive, NLP, and generative AI methods. Partners with technical and business stakeholders, communicates insights and recommendations, monitors solution value, and provides technical guidance. The role supports product, manufacturing, warranty, safety, and customer quality decisions in a regulated, office-based environment.

Build, deploy, monitor, and improve production-grade AI agents using Python, Claude Agent SDK, and AWS Bedrock AgentCore. Design golden datasets and LLM-as-a-Judge evaluations, integrate automated quality gates into CI/CD, and implement Langfuse or OpenTelemetry observability. Evaluate agent accuracy, task completion, tool execution, and performance while supporting RAG pipelines and AWS data workflows. The role also involves troubleshooting, continuous evaluation, and potentially agentic red-teaming.

Develop AI agents, prompts, workflows, contextual data layers, orchestration frameworks, and machine learning models. Test and optimize solutions using structured and unstructured data, evaluate accuracy and scalability, analyze edge cases and failure modes, and document experiments and model performance. The 10-week remote internship is intended for students at least in their third year studying data science, AI, computer science, or a related field.