Top AI & Machine Learning Jobs

Leads end-to-end AI enablement consulting engagements, including readiness assessments, governance frameworks, champion programs, workshops, use-case discovery, adoption change management, and training. Develops executive-ready strategies, roadmaps, risk models, dashboards, and other client deliverables. Advises senior AI leaders, mentors junior staff, supports AI solution deployment, evaluates emerging tools, and contributes to business development and revenue growth.

Build and integrate machine learning systems using real-world sensor, document, and operational data. Responsibilities include developing Python data pipelines, training and evaluating models, integrating models or LLM workflows into tools, investigating failure modes, documenting results, and using version control, testing, code review, and experiment tracking. The intern will own a scoped project and present findings and recommendations at the end of the summer.

Designs and deploys production-ready AI solutions for strategic enterprise customers using Genesys Cloud, agentic AI, conversational AI, automation, data, and integrations. Leads discovery, architecture, prototyping, deployment, optimization, governance, and value realization. Advises executive stakeholders, connects technical decisions to business KPIs, develops reference architectures, guides responsible AI practices, and builds customer capability across complex enterprise environments.

Explore, prototype, and productionize AI solutions for clinical data review, focusing on LLMs, agentic AI, RAG, and intelligent workflows. Build modular agent systems, APIs, and interfaces; integrate clinical data sources; evaluate and monitor AI performance; and ensure scalable, reproducible, traceable, auditable solutions aligned with regulatory expectations. Collaborate with clinical experts, product managers, and engineering teams while contributing to AI innovation strategy.

Collect, clean, and analyze sports data; develop machine learning and predictive models; create visualizations and reports; collaborate with data and sports strategy teams; and monitor advances in sports analytics.

Leads the direction and execution of Modal’s LLM inference platform. Responsibilities include managing and growing an engineering team, making technical and product decisions, guiding distributed systems and inference-serving architecture, partnering with frontier customers, shaping optimization and product roadmaps, establishing reliability standards, and collaborating with compute strategy and go-to-market teams.

Define clinical reasoning, safety boundaries, evaluation scenarios, datasets, rubrics, and acceptance thresholds for AI-enabled chronic disease products. Analyze failures and edge cases, support clinical validation and outcomes research, interpret clinical and EHR data, and collaborate with Product, Engineering, AI/ML, Data, informatics teams, and clinical stakeholders on integrations and workflows.

This role involves designing, developing, and maintaining AI/ML products, collaborating with stakeholders, and ensuring model deployment on AWS. Responsibilities include creating APIs, establishing MLOps practices, and driving AI strategy for operational improvements.

Build backend infrastructure for an AI revenue engine. Integrate large language models, optimize data pipelines, and develop scalable systems supporting autonomous revenue workflows for companies in the physical economy.

Design, build, and deploy AI-powered features for a digital banking platform, including natural language interfaces, assistants, and automated workflows. Integrate LLMs and AI APIs using prompt engineering, tool calling, validation, and safety guardrails. Evaluate AI outputs for accuracy, reliability, and regulatory compliance; collaborate cross-functionally; document best practices; conduct code reviews; and assess emerging AI tools and frameworks.