Top AI & Machine Learning Jobs

Build lightweight AI prototypes and workflow solutions for portfolio companies. Identify operational bottlenecks, develop proof-of-concepts using large language models and emerging AI technologies, document technical requirements for engineering handoff, and collaborate with investment, management, and engineering teams to drive AI adoption, operational efficiency, and business ROI.

Designs and leads end-to-end AI solutions across clinical, operational, and enterprise workflows. Partners with product, data science, engineering, cloud, and clinical teams to develop scalable architectures spanning data pipelines, models, MLOps, governance, monitoring, and EHR integration. Establishes standards for safety, reliability, observability, bias, and performance; guides build-versus-buy decisions and vendor integrations; creates reference architectures; and delivers measurable improvements in outcomes, efficiency, and user experience.

Leads the architecture, development, and deployment of enterprise Generative AI and agentic systems for mortgage lending. Builds RAG pipelines, multimodal applications, cloud integrations, and LLMOps capabilities. Mentors engineers, guides technical strategy, evaluates emerging AI technologies, and collaborates with product, data, and UX teams to deliver scalable solutions. Requires strong software engineering or data science experience, expertise in LLM systems, cloud platforms, AI governance, model safety, and prompt engineering.

Evaluate and validate machine learning outputs against financial theory, client objectives, and market dynamics. Translate research signals into investment strategies, portfolio construction, and testable modeling specifications. Design and scale quantitative models in Python and Java, develop financial validation frameworks, and collaborate with ML researchers and technical teams to integrate research into production. Communicate quantitative insights clearly across technical and business stakeholders.

Design and build a next-generation analytics engine and cost-based query optimizer delivering major performance improvements over cloud data warehouses. The founding team member will develop data-learning optimization technologies, ship cloud data products through multiple iterations, and work across relational databases, NoSQL, data lakes, and OLAP systems. Responsibilities include rapid experimentation, debugging, collaboration, and product development in a startup environment.

Designs, develops, deploys, and maintains secure AI-agent and automation solutions for enterprise and client operations. Builds LLM workflows, backend integrations, AWS serverless infrastructure, event-driven pipelines, database connections, monitoring, and deployment systems. Translates ambiguous business processes into reliable, validated, auditable solutions with human approval controls. Participates in stakeholder discovery, technical documentation, testing, deployment, incident resolution, and continuous improvement.

Build and own the agent harness, evaluation workflows, backend services, APIs, integrations, data models, and system architecture powering Petual’s AI control-testing platform. Develop reliable long-horizon agents, process sensitive evidence securely, integrate with enterprise systems, and deliver auditor-ready outputs. Partner directly with customers while contributing across backend and frontend development, infrastructure, security, and product decisions.

The Director of Product and Solutions Management will lead AI-driven healthcare services, overseeing product lifecycle, development, and team management. Responsibilities include creating strategic roadmaps, optimizing service delivery, and fostering innovation within the team.

Own and operate the company’s enterprise AI platform across Google Cloud, Claude Enterprise, and Gemini Enterprise. Responsibilities include AI platform operations, CI/CD, infrastructure automation, observability, identity and user lifecycle management, marketplace extensions, internal agents and tooling, AI governance, evaluation, onboarding, documentation, and enablement. The role requires 3–5 years of platform, DevOps, SRE, MLOps, backend, or infrastructure experience and is onsite four days weekly in Chicago.

Supports global supply chain and operations through data cleaning, exploratory analysis, machine learning forecasts, anomaly detection, KPI analysis, predictive modeling, and dashboard development. The intern uses Excel and Power BI to create reports and visualizations, identifies data quality issues, collaborates with cross-functional teams, and presents actionable recommendations to stakeholders and management.