Top AI & Machine Learning Jobs

Build and deliver production-quality AI agents and LLM-powered applications for business clients. Own solutions end to end, including workflow redesign, architecture, development, testing, deployment, adoption, and presentation. Apply prompt engineering, data modeling, automation frameworks, and enterprise data expertise. Evaluate emerging AI tools, create reusable delivery patterns, document standards, and guide team members while translating technical decisions for executive stakeholders.

Lead data science for Mozilla’s advertising business by identifying high-impact opportunities, owning complex analyses, developing measurement and experimentation frameworks, analyzing revenue and performance, and translating findings into recommendations for technical partners and executives. Partner across Product, Engineering, Sales, Finance, and leadership to support business decisions in a fast-moving environment while balancing analytical rigor, pragmatism, and user privacy.

Lead AiLO portfolio integrating AI into K-12 systems: set strategy, design and draft high-stakes deliverables, manage complex projects, mentor teams, cultivate partnerships, and represent ILO externally to scale responsible AI adoption in education.

Hands-on senior full-stack engineer leading multiple projects to build and maintain React/Angular frontends and Node.js backends, integrate LLMs and AI/ML (LLM APIs, computer vision, vector search/RAG), manage cloud infrastructure (Azure/AWS), enforce AI-assisted development practices, conduct code reviews and mentoring, support production reliability and on-call incident response.

Prospect and engage small-business owners through in-person visits, cold calls, email, online research, social media, networking, and referrals. Understand prospect needs, present the platform, schedule demos, guide prospects through the sales process, maintain accurate CRM records, meet weekly sales goals, and share customer feedback with the founding team. The role requires resilience, initiative, adaptability, strong communication, and comfort working in an evolving startup environment.

Leads Central Health System’s data science team and serves as its senior technical authority. Develops and validates predictive, machine learning, NLP, and forecasting models supporting population health, care management, and clinical decision-making. Oversees AI governance, model risk assessment, fairness, reproducibility, documentation, and regulatory alignment. Partners with clinical, data engineering, governance, and analytics teams while mentoring staff and managing advanced analytical standards across Epic, Azure, Snowflake, and other enterprise data sources.

Designs and operates production multi-agent AI systems, LLM workflows, scoring and optimization algorithms, and closed-loop autonomous processes. Responsibilities include AI model evaluation, production monitoring, governance, third-party AI integration, federated data retrieval, and identifying patentable inventions. The role requires deep machine learning expertise, algorithm development, and deployment experience across complex operational environments.

Build and deploy Palantir Foundry workflows, data pipelines, ontology models, AI/ML and LLM applications, integrations, and automation for internal and portfolio operations. Partner directly with business teams to turn prototypes into production systems, establish reusable engineering patterns, manage security and governance, measure adoption and ROI, and train users. The role requires strong Python engineering, enterprise data-platform experience, production generative AI delivery, and the ability to independently execute ambiguous technical initiatives.

Leads the foundation’s responsible AI portfolio, strategy, governance, policy development, model evaluation, vendor assessment, and production deployment. Designs ethical AI solutions using modern machine learning and generative AI technologies, while optimizing performance, cost, scalability, and sustainability. Partners with executives, technical teams, vendors, and nontechnical staff to build AI literacy, communicate complex concepts, manage risks, and promote fairness, transparency, privacy, and responsible innovation across the organization.

Build and deploy custom AI workflows, automations, and agentic systems for clients. Collaborate with executives and frontline teams to understand processes, identify AI opportunities, test solutions, measure business impact, and document reusable playbooks. Support client strategy meetings, help improve company offerings, and manage projects and communications in a fast-paced early-stage environment.