Top AI & Machine Learning Jobs

Develop and maintain credit risk models for origination, behavioral risk, underwriting, and portfolio management across Latin America. Own the full modeling lifecycle, including feature engineering, validation, backtesting, monitoring, calibration, and implementation. Analyze large financial datasets using Python and SQL, monitor portfolio and model performance, address data quality and selection bias, and translate analytical findings into credit strategies. Collaborate with Risk, Data, Engineering, Finance, and Operations teams.

Build and operate production agentic AI systems embedded within business units. Identify high-value automation opportunities, integrate CRM and business application APIs, design orchestration and evaluation frameworks, deploy monitored workflows, and measure ROI. The role requires close collaboration with GMs, sales, operations, customer success, and field teams while contributing reusable AI engineering patterns to a shared library.

Build and improve generative AI applications using LLMs, RAG workflows, APIs, databases, and backend components. Write tests, debug failures, improve performance and reliability, and collaborate on integration, deployment, and monitoring. The role also involves connecting models to external data sources and supporting scalable, reliable AI systems.

Builds and deploys AI-enabled applications, machine learning models, data pipelines, APIs, and decision-support tools for aggregate and asphalt operations. The role optimizes plant automation, production efficiency, uptime, quality, pricing, sales processes, and costs using operational, equipment, quality, GIS, and sales data. It integrates AI with plant systems and legacy software, collaborates across operations and IT, and ensures responsible AI, cybersecurity compliance, documentation, and user adoption.

Entry-level role supporting a law firm's Structured Finance & Derivatives group by evaluating and implementing generative AI tools, developing prompts and AI-powered workflows, testing outputs, researching emerging technologies, and identifying automation opportunities in legal practice. The position collaborates closely with attorneys and technology professionals and is designed for a tech-savvy graduating senior or recent graduate interested in AI, law, finance, or legal technology.

Own Workhelix’s analytical engine end to end, including customer data integration, ETL, data modeling, AI opportunity assessments, predictive analyses, visualizations, and production deployment. Build Python and SQL systems running in cloud infrastructure, develop customer-specific analyses, create internal analytical tools, and shape the data science roadmap. Partner with engineers, strategists, executives, and customers while setting standards for analytical rigor and translating research into practical business decisions.

Lead Glacier’s distributed computer vision engineering team, owning the CV roadmap, technical priorities, execution, hiring, mentoring, and cross-functional collaboration. Guide model, dataset, compute, evaluation, and production-performance decisions without serving as a day-to-day developer. Oversee labeling operations and coordinate with Product, Software, Operations, Manufacturing, and Field Engineering to deliver computer vision systems for recycling robots and customer sites.

Conduct research on difficult technical problems and develop, test, and improve algorithms spanning AI, machine learning, statistics, optimization, estimation, signal processing, and related fields. Review academic literature, analyze complex data, compare approaches, and adapt research methods to real-world Department of Defense applications. Collaborate with scientists and engineers to integrate algorithms into operational systems, communicate findings, and identify emerging research opportunities.

Lead strategy, roadmap, and lifecycle for AI-driven products. Collaborate with engineering, legal, IT, and marketing to define features, integrate AI models, ensure responsible AI practices, set KPIs, and deliver high-value production AI solutions.

Teach computer science and information technology using hands-on, project-based curriculum. Cover cybersecurity and AI fundamentals, prepare students for industry certifications and postsecondary pathways, maintain equipment, liaise with industry partners, support student job placement, and participate in marketing and advisory activities.