Top AI & Machine Learning Jobs

Research, architect, and ship AI-powered product capabilities using agents, LLMs, RAG, embeddings, and machine learning. Build scalable multi-tenant AI infrastructure, agentic chat systems, data integrations, observability, evaluation frameworks, and guardrails. Translate business needs into measurable user-facing solutions while collaborating with product, data science, engineering, and leadership teams on the AI roadmap.

Project-focused AI engineering internship embedded on a DevOps team. The intern will prototype generative assistants for alert response, build retrieval-augmented systems over permissioned internal data, design guarded agentic remediation workflows, and support internal engineering Q&A. Responsibilities include tool-use workflows, observability integration, evaluation, documentation, Git-based delivery, and strict controls for permissioning, auditing, logging, safety, and cost.

Leads enterprise AI transformation by developing intelligent agents and automation for Finance, HR, and other corporate functions. The role integrates AI with ERP, HRIS, financial, payroll, workflow, and business intelligence systems to monitor compliance, enforce processes, detect risks, automate activities, and deliver executive insights. Responsibilities include AI governance, security, privacy, auditability, cross-functional leadership, business impact measurement, organizational change, and building an AI-focused team.

Develops, evaluates, deploys, and optimizes AI and machine learning solutions for healthcare and business applications. Responsibilities include predictive analytics, natural language processing, image recognition, AI pipelines, clinical decision support, data preparation, model validation, compliance, documentation, stakeholder collaboration, staff training, vendor evaluation, and ongoing model monitoring. Provides limited rotational on-call and help desk support for healthcare data reporting needs.

Supports AI governance and compliance by monitoring global regulations, developing compliance roadmaps, gathering evidence across product and software development lifecycles, maintaining policies and model documentation, and updating risk registers. Builds AI agents and automations, contributes to internal training, supports RFP responses, and coordinates public AI Trust Center content while collaborating with Product, Engineering, Security, Legal, Data Science, and Sales.

Assists with the development, testing, deployment, and optimization of generative AI and LLM-based applications. Supports prompt engineering, model fine-tuning, foundation model integration, cloud deployment, documentation, and business use-case implementation. Collaborates with product managers, software engineers, cloud architects, and stakeholders while researching emerging AI technologies and contributing to scalable, responsible AI solutions.

Build and deploy multimodal AI agents for essential service businesses. Partner with enterprise customers to map workflows, implement solutions, manage rollouts, measure outcomes, and iterate rapidly. Develop scalable, reliable systems supporting thousands of concurrent interactions; evaluate and fine-tune language and speech models, run experiments, optimize performance, and contribute insights to product and engineering roadmaps.

Annotate and manage training/test datasets, run model training, evaluate model performance, perform manual QA and error analysis, and refine annotation guidelines for NLP/ML projects.

Build, deploy, and maintain AI agents and automated workflows across business functions. Integrate solutions with enterprise systems, monitor performance, manage AI infrastructure, and participate in governance. Analyze workflows, run pilots, measure ROI, establish risk controls, and support adoption through training and documentation. Partner with stakeholders across departments while maintaining privacy, security, and regulatory controls.

Develop and productionize machine learning and hybrid physics-ML methods for seismic imaging, inversion, and velocity model building. The role applies deep learning, generative models, neural operators, and physics-informed approaches to large seismic datasets using GPU and cloud infrastructure. Responsibilities include validating methods against physics-based approaches, quantifying uncertainty, documenting reproducible workflows, and communicating results to geophysicists, data scientists, and asset teams.