Top AI & Machine Learning Jobs

Build Preql’s core agentic AI workflows for financial data cleaning and transformation. Responsibilities include developing LLM-powered data pipelines, conversational interfaces, audit-ready transformation engines, APIs, financial data integrations, and web-based workflow builders. The role owns the full stack, collaborates directly with enterprise customers, and takes the product from proof of concept to MVP while ensuring data lineage, reproducibility, and compliance.

Design, build, and deploy production-grade agentic AI systems, including autonomous and multi-agent architectures, tool-use orchestration, memory, retrieval, and function-calling workflows. Integrate agents with enterprise tools and databases, implement evaluations, guardrails, observability, fallback paths, and human-in-the-loop controls. Lead technical strategy, client collaboration, prototyping, and product direction for enterprise AI initiatives.

Analyze federal workforce data and position descriptions using statistical modeling, NLP, machine learning, and text analytics. Develop dashboards, visualizations, and reports; map duties and skills to job series and grade levels; validate findings with HR professionals and leadership; and provide recommendations supporting accurate, compliant job classification and reclassification decisions.

Own the architecture, roadmap, and production operation of search and recommendation systems across discovery surfaces. Lead retrieval, ranking, personalization, relevance measurement, experimentation, OpenSearch infrastructure, signal pipelines, and candidate generation. Partner with Product, Data, ML, and Trust & Safety to balance relevance, freshness, safety, latency, and cold-start behavior. Establish engineering standards, instrumentation, rollout processes, and runbooks while mentoring backend engineers.

Build and ship practical AI-powered tools, dashboards, bots, automations, and workflow integrations using Claude and Gemini APIs. Translate operational problems into scoped solutions, connect AI to business systems, iterate based on user feedback, and document every tool with a runbook. Responsibilities include agent and workflow design, MCP connectors, prompt evaluation, regression testing, API cost management, custom coding, and training internal users.

Design, build, deploy, and support enterprise AI agents, copilots, intelligent workflows, and lightweight applications. Integrate AI solutions with APIs, Microsoft 365, SharePoint, CRM systems, and business data while ensuring security and scalability. Translate business needs into practical solutions, prototype emerging technologies, improve agent performance, and collaborate with stakeholders to drive user adoption and measurable business value.

Graduate intern supporting AI-driven automation for CFD-in-the-loop design optimization of engine and propulsion technologies. Responsibilities include developing simulation and optimization workflows, applying machine learning, surrogate modeling, Bayesian optimization, active learning, and design-of-experiments methods, and using CAD, meshing, CFD tools, and HPC environments. The intern will collaborate with research scientists and contribute to analytical, documentation, and communication activities.

Designs and implements large-scale software frameworks for computer vision and machine learning across embedded, mobile, desktop, and cloud environments. Leads open-source software quality and code review efforts, integrates third-party libraries, optimizes complex systems, and supports distributed and GPU-based computing. The role contributes to research and development in areas including 3D reconstruction, object detection, image manipulation detection, and anomaly detection.

Build and own Python-based performance and power modelling frameworks for photonic AI hardware. Model concurrency, event-driven behavior, compute, memory, interconnects, and accelerator blocks; characterize AI workloads against hardware and RTL results; consume MLIR and compiler outputs; analyze simulation traces and benchmarks; validate power models; maintain CI/CD and simulation flows; and guide architecture and compiler decisions while developing future team members.

Build and ship production AI-powered development features, including agentic workflows, LLM pipelines, retrieval systems, evaluations, fine-tuning, and latency optimization. Own AI feature quality from prototyping through deployment and monitoring. Collaborate with Product, Infrastructure, and Research teams to scale AI capabilities for Bubble’s visual app-building platform.