Top AI & Machine Learning Jobs

Develop AI communication systems for humanoid robots, including generative AI, NLP, speech-to-text, text-to-speech, computer vision, multimodal interaction, gesture recognition, and emotion-aware capabilities. Optimize transformer-based models for real-time edge execution and integrate speech, vision, and movement for natural human-robot interactions.

Research Engineer responsible for developing, training, evaluating, and deploying healthcare AI models and pipelines for conversational voice agents. The role focuses on instruction following, tool calling, retrieval, memory, speech and audio research, model optimization, benchmarking, failure analysis, and production integration. Candidates should have 5+ years of experience, including AI/ML engineering or research, production LLM deployment, Python proficiency, and a record of converting research prototypes into reliable measurable improvements.

Build a personalized provider-recommendation pipeline using LLMs, semantic search, recommendation algorithms, agent workflows, and multi-source knowledge integration. Responsibilities include extracting user requirements, retrieving and ranking providers, connecting backstage decision pipelines, developing evaluation datasets, analyzing failures and feedback, and improving quality, reliability, and latency. The intern will collaborate with engineering and product teams from design through launch.

Build production AI-powered software for hiring, finance, capital planning, contract analysis, and operational workflows. Develop LLM features, MCP servers, and agentic systems; embed with finance, legal, recruiting, accounting, and operations teams; translate expert workflows into structured data and automated processes; and ship autonomous, user-friendly products rapidly.

Own backend architecture, code, deployments, monitoring, and on-call for AI shopping agents that perform live purchases. Build resilient checkout automation, scalable product ingestion infrastructure, embedding-based search and ranking, and systems that automatically detect and recover from failures. The role requires strong Python, end-to-end production ownership, and comfort with ambiguous, adversarial systems. Experience with AI agents, LLMs, machine learning, e-commerce, payments, or fraud infrastructure is preferred.

Advises clients on applying Microsoft AI tools to business needs, identifies high-value use cases, designs Copilot agents and AI-enabled solutions, facilitates readiness workshops, supports adoption and change management, and measures productivity and business value. The role also contributes to AI governance, training content, stakeholder presentations, presales solutioning, prompt engineering, and ongoing Microsoft AI strategy updates.

Advises clients on applying Microsoft 365 Copilot and related AI tools to business processes. Identifies use cases, designs Copilot solutions and agents, leads readiness and envisioning workshops, supports governance and change management, develops training content, and tracks adoption and ROI. Partners with technical teams, sales, and client stakeholders to translate AI capabilities into measurable productivity, cost, risk, and operational benefits.

Analyzes and reconciles data across systems, builds and maintains data pipelines, develops predictive and machine-learning models, and creates automated AI workflows for security intelligence, anomaly detection, and reporting. The role also develops LLM prompts, evaluates model quality, supports cloud and containerized infrastructure, contributes to CI/CD automation, monitors controls, creates SOPs, and provides analytical guidance to clients and management.

Lead the design, testing, governance, and implementation of AI-assisted workflows across Finance, Tax, Treasury, and Risk. Responsibilities include prompt and agent logic development, process mapping, requirements gathering, AI output validation, automation documentation, stakeholder collaboration, production transition, troubleshooting, and training. The role supports scalable, compliant finance transformation using tools such as Microsoft Copilot, Claude, Alteryx, and UiPath.

Provide data analysis, graphics, and dashboards to identify challenges and opportunities. Use Python or R, SQL, relational and NoSQL databases, Tableau, and time-series analysis to transform unstructured data into actionable insights supporting customer decisions about production, resources, and personnel.