Top AI & Machine Learning Jobs

Leads the design, implementation, architecture, governance, and scaling of agentic automation solutions. Builds production agents using Microsoft Power Platform, Azure AI Foundry, browser automation, LLM reasoning, and Python. Establishes enterprise standards for agent reliability, security, recovery, hosting, monitoring, auditability, and human escalation while partnering with Security, Risk, and Compliance.

Leads ElectroKare’s scientific vision and machine learning research agenda for EKG waveform modeling and clinical prediction. Oversees ML engineers and researchers, guides sequence architecture decisions including Mamba, translates healthcare problems into ML solutions, partners on FDA validation, and represents the company’s science externally. Requires applied ML/DL research leadership, clinical or biomedical data experience, modern sequence modeling expertise, and advanced education or equivalent industry depth.

Supports the development and deployment of AI solutions and machine learning models. The intern helps design scalable applications, build data and AI pipelines, write reliable code, troubleshoot performance issues, participate in the SDLC, collaborate with technical teams, and document completed work.

Supports the development and deployment of AI-driven healthcare solutions using Palantir Foundry, Python, SQL, and TypeScript. Assists with data workflows, analytics, AI integration, and application testing while collaborating with clinicians, staff, researchers, executives, and technical teams. The internship includes mentorship and hands-on work translating healthcare problems into data and AI solutions.

One-year AI research fellowship for B.Tech, M.Tech, and PhD students offering a monthly stipend, research project, industry internship, computing resources, and conference support. Fellows submit resume, SOP, research proposal, and recommendations.

Paid summer data science internship supporting banking analytics through data querying, statistical analysis, customer behavior and profitability reporting, campaign analysis, experimental design, and presentation of findings to data scientists and business leaders. The role runs for 11 weeks on a full-time Monday-through-Friday schedule with a hybrid arrangement requiring two in-office days per week.

Build and optimize edge and cloud data pipelines for perception ML models. Scale data engine for high-fidelity labels, reduce annotation costs, integrate foundation models for automated labeling and QA, leverage software/hardware-in-the-loop testing, and support DoD field use cases and model deployment lifecycle.

Lead the engineering team building Chariot’s AI claims platform, including agent systems for case origination and claims matching, human-in-the-loop review, evaluation infrastructure, and scalable AI performance. The role owns product decisions, internal tooling, cost efficiency, and production reliability while recruiting and developing full-stack and ML engineers. Experience with production software, end-to-end product ownership, early-stage startups, and modern LLM systems is valued.

Build, scale, and operate production-grade machine learning systems supporting real-time personalization. The role involves developing scalable data analysis and model-development processes, validating and implementing models, protecting system quality through testing, collaborating cross-functionally, and leading machine learning initiatives. The engineer will work extensively with Python, modern ML frameworks, large-scale data systems, and AWS-based infrastructure while providing technical leadership.

Design, develop, and operationalize AI and machine learning solutions for production applications and data workflows. Build data and model pipelines, implement MLOps and LLMOps practices, support generative AI and RAG systems, and contribute to CI/CD, observability, responsible AI, governance, and model lifecycle management. Collaborate with data science, software engineering, and cloud teams in secure federal environments.