Top AI & Machine Learning Jobs

Build and evolve front-end and full-stack experiences that bring AI capabilities to enterprise contract users. The role involves designing intuitive interfaces, contributing across client and server-side technologies, shaping product direction, and solving complex problems in a fast-changing startup environment. Engineers will use JavaScript/TypeScript, React, Python, SQL, databases, CI/CD, Git, and OpenAI or Anthropic generative AI models.

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.

Leads AI data engineering and actuarial analytics delivery, including Snowflake data products, pipelines, semantic layers, AI agents, dashboards, governance, and quality frameworks. Manages a complex technical team, modernizes legacy data ecosystems, establishes architecture and engineering standards, evaluates emerging AI technologies, manages portfolio budgets, and partners with actuaries, data scientists, IT, architecture, and business stakeholders to deliver production-ready analytics and AI solutions.

Own the architecture and technical direction of a production agentic AI product. Lead agent orchestration, context pipelines, evaluation infrastructure, search, grounding, security, cloud platform decisions, and enterprise integrations. Establish quality standards, improve engineering practices, mentor senior engineers, guide hiring, and remain hands-on solving complex technical problems across React, services, and PostgreSQL.

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.

Own end-to-end architecture for large-scale AI and distributed systems from concept through production. Design RAG and Generative AI solutions, establish architecture standards, evaluate technologies, and guide engineering teams. Partner with business and technical leaders to translate requirements into scalable, secure, resilient platforms. Address AI lifecycle management, model selection, evaluation, monitoring, latency, cost, privacy, and guardrails. Develop architecture documentation, prototypes, and production solutions using cloud-native, data, API, and DevOps technologies.

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.