Top AI & Machine Learning Jobs

Supports the rollout and adoption of AI capabilities across Capital Partners by delivering workshops, onboarding, guidance, playbooks, and learning materials. Coordinates AI pilots, product releases, change plans, and readiness activities; maintains use-case inventories and knowledge assets; gathers user feedback; monitors adoption metrics; and recommends improvements. The role also facilitates process mapping, supports responsible AI usage and governance, manages stakeholder follow-through, and helps move AI initiatives from pilot to production-ready solutions.

Develop and optimize large-scale machine learning models, open-source LLM infrastructure, and training and inference systems for advertising applications. Build distributed data and model pipelines, adapt DeepSpeed and OpenRLHF, optimize GPU/TPU serving for latency and cost, monitor model performance, and collaborate with applied scientists on research. The role focuses on recommender systems, personalization, targeting, reinforcement learning, and ad intelligence at massive scale.

Build Python data pipelines for automated perovskite solar panel characterization. Extract electrical performance metrics from raw J-V data, classify device anomalies, generate pass/fail reports, and correlate electroluminescence image metrics with IV performance. The intern will establish panel screening criteria, document results, collaborate with an engineer mentor, and present project accomplishments to leadership.

Deploys Medeloop’s AI systems into customer cloud environments, builds data and EHR connectors, and creates clinical and research workflows. Owns deployments from scoping through production, including evaluation, testing, monitoring, security reviews, and customer collaboration. Works with sensitive patient data under HIPAA, explains infrastructure and access controls to security teams, and converts custom implementations into reusable platform features.

Lead end-to-end AI/ML initiatives to improve student retention, engagement, and enrollment. Architect and operate data pipelines, feature stores, and production ML systems; deploy, monitor, and automate models; run experimentation programing and measure business outcomes. Mentor team members and set technical standards across Product, Engineering, and CX.

Leads hands-on design, development, testing, and production delivery of generative and agentic AI solutions. Builds RAG, automation, knowledge retrieval, developer productivity, and IT operations capabilities; integrates AI with enterprise systems, APIs, data, and cloud services. Ensures security, privacy, observability, evaluation, scalability, and operational readiness while mentoring engineers, documenting reusable patterns, and coordinating technical delivery across architecture, infrastructure, security, data, and business teams.

Own the end-to-end lifecycle of generative AI products across Success Academy’s school network. Translate stakeholder and business needs into AI/ML requirements, collaborate with engineering, data science, and UX teams, integrate LLMs and RAG pipelines, define evaluation metrics, run experiments, build user feedback loops, and optimize products for measurable efficiency and student outcomes. Contribute to enterprise AI strategy and manage products from discovery through launch.

Support a government customer as an expert AI/ML Engineer by developing advanced AI/ML architectures, responsible AI implementations, enterprise deployment strategies, and roadmaps advancing technologies from research to operational deployment. The role requires expertise in neural networks, reinforcement learning, model interpretability, MLOps pipelines, productionizing data science research, and deploying GPU-enabled deep learning applications.

Leads the AI Governance Program for a regulated mortgage lending division. Establishes governance structures, policies, risk-tiering methods, controls, use-case lifecycle processes, third-party AI oversight, and audit-ready documentation. Coordinates reviews across legal, compliance, security, privacy, data governance, model risk, vendor management, and enterprise risk teams. Reports AI governance KPIs and KRIs to senior leadership while providing independent challenge and ensuring AI systems meet regulatory and organizational requirements.

Design and build production AI applications, agentic workflows, RAG pipelines, and multi-step automations using LLM APIs and orchestration frameworks. Integrate tools, retrieval systems, prompts, APIs, and infrastructure; create evaluation and observability systems; monitor reliability, latency, cost, and output quality. Translate ambiguous business needs into practical technical solutions, collaborate with stakeholders, and identify opportunities for AI-enabled process improvement.