Top AI & Machine Learning Jobs

Supports enterprise AI governance through system intake reviews, risk and control analysis, scorecard maintenance, framework research, inventory management, reporting, AI literacy, stakeholder workshops, documentation, and governance workflow improvements. The intern gains experience with responsible AI, NIST AI Risk Management Framework concepts, ISO/IEC 42001, Microsoft Copilot, AI adoption, and governance operations.

Analyze multimodal healthcare, genomic, clinical, and biomedical imaging data to support drug discovery, biomarker development, disease progression modeling, clinical trial design, and real-world evidence analyses. The role involves building statistical and machine learning models, developing data pipeline modules and interactive tools, collaborating with clinical, genomics, and biology teams, contributing to data strategy, and communicating findings through publications and stakeholder presentations.

Provides strategic and technical leadership for enterprise AI, machine learning, generative AI, and advanced analytics. Establishes AI roadmaps, standards, governance, and responsible AI practices; oversees model, agent, platform, and data solution development; and leads MLOps, infrastructure, optimization, and lifecycle management. Partners with executives and business leaders to deliver measurable outcomes, manages budgets and vendors, and builds high-performing AI/ML and analytics teams.

Leads technical foresight on how AI and emerging technologies may reshape global finance. Assesses AI capabilities, architectures, scaling, constraints, governance, failure modes, and systemic risks; translates findings into scenarios, hypotheses, briefings, research priorities, and thought leadership. Partners with research, standards, advocacy, and industry stakeholders to stress-test assumptions and evaluate technology’s implications for investment workflows, institutions, markets, professional roles, and standards.

Leads consumer personalization strategy and production development for recommendations, ranking, search, browse, carts, marketing, and agentic AI experiences. Builds recommender systems, learning-to-rank models, LLM agents, evaluation frameworks, and experimentation programs. Partners with Product and Engineering on deployment, monitoring, and model quality while mentoring senior data scientists, setting technical direction, shaping roadmaps, and communicating trade-offs to executives.

Build and operationalize production-grade image and video generative AI systems. Responsibilities include designing multimodal pipelines, agentic workflows, MCP servers, microservices, prompt management, model integrations, ML infrastructure, and scalable inference services. The role emphasizes reliability, cost, latency, observability, and collaboration with data scientists, designers, product managers, and creative teams to deliver AI-powered visual experiences.

Lead the architecture and development of MHC’s data platform, including ingestion, modeling, governance, pipelines, and feature stores. Build production AI and LLM capabilities such as document understanding, RAG, embeddings, evaluation, and intelligent product features. Manage and mentor a small data and AI engineering team, establish technical standards, define roadmaps, and partner with Product, Engineering, and business stakeholders.

Lead AI product strategy and deliver AI-powered features for a healthcare workforce SaaS. Translate AI concepts into user experiences, run discovery with customers, collaborate with data science and engineering, use LLM tools in daily workflows, define success metrics and experiments, ensure ethical AI practices, and evangelize AI capabilities internally and to customers.

Analyze structured and unstructured data to identify insights, develop algorithms and data systems, and support client decision-making. Apply statistical and scientific methods using R or Python, perform qualitative and quantitative analyses, and work with disparate data sources. Collaborate with clients and team members in a fast-paced environment while using data gathering, cleansing, and parsing techniques across Windows and Linux environments.

Design and build agent-based AI systems, including single- and multi-agent architectures, tool-use workflows, feedback loops, planning, memory, delegation, evaluation, and autonomous processes. Improve reliability, observability, and failure recovery while exploring long-running and self-improving systems. Candidates should independently build and test projects, evaluate agent performance, follow current AI research, and understand how AI components interact.