Top AI & Machine Learning Jobs

Architect and build AI reasoning systems, agentic workflows, and multimodal intelligence pipelines for regulatory, financial, and spatial analysis. Develop production-grade LLM applications, integrate validation and evaluation frameworks, process scanned documents and schematics, and own AI-powered features across models, backend workflows, and customer-facing interfaces. The role emphasizes reliability, observability, autonomous ownership, and full-stack product development within a remote founding team.

Designs, builds, evaluates, and deploys machine learning, LLM, and agentic AI capabilities for cybersecurity detection, investigation, and response. Analyzes complex security data, develops models and evaluation frameworks, creates RAG and retrieval workflows, and partners with engineering teams to productionize and monitor AI systems. The role requires addressing adversarial, noisy data and balancing accuracy, explainability, latency, reliability, and operational impact.

Supports enterprise growth through strategic market research, business analytics, account intelligence, and AI innovation. The intern will research companies and competitors, analyze market trends, prepare strategic presentations, and help design and test AI agents and intelligent workflows for automated research and reporting. The role collaborates with Sales and Marketing while identifying ways to improve productivity and decision-making using generative AI and emerging technologies.

Develop and deploy machine learning algorithms for defense and commercial applications, working with images, text, graph data, NLP, computer vision, reinforcement learning, and large language models. Responsibilities include analyzing large datasets, building inference models, using cloud services for training and deployment, researching emerging methods, and preparing technical documentation. The role requires Python, machine learning experience, a relevant bachelor’s degree, at least two years of experience, and an active TS/SCI clearance with CI Poly.

Build, train, evaluate, and deploy deep learning models for credit modeling and loss prediction. Conduct representation learning experiments, integrate models into ML pipelines, and own projects from hypothesis through measurable business outcomes. Collaborate with data scientists, engineers, and product partners, communicate findings, and develop agentic tools that accelerate data science workflows.

Build and maintain AI-powered workflows, agents, automations, internal tools, and production systems for creative ideation, copy generation, video pipelines, reporting, and client delivery. Integrate LLMs and third-party APIs, ship rapidly, iterate based on real usage, and develop practical tools that improve marketing operations.

Develop and execute the organization’s AI strategy, roadmap, policies, and responsible-use guidelines. Evaluate and implement AI solutions, measure business impact, establish governance for privacy and security, and identify opportunities to improve productivity. Educate employees on AI capabilities, prompting, and responsible use while partnering with technical and nontechnical teams to drive adoption. The role is onsite in Des Moines and requires travel to regional construction jobsites several times monthly.

Graduate intern supporting filtration research and product development through Python-based engineering workflows, data preparation, simulation analysis, machine learning models, parameterized geometry, and technical documentation. The role involves collaborating with engineers and data scientists, evaluating simulation and laboratory results, developing reproducible applications, and communicating findings. Work is on-site in Bloomington, Minnesota, with travel of up to 5%.

Own end-to-end technical architecture and product execution for an AI-powered scientific research workspace. Build production agent systems using Next.js, Python, FastAPI, retrieval, and infrastructure; improve reliability, quality, and observability for high-stakes workflows; work directly with enterprise researchers and customers; make product tradeoffs; and build the engineering team and culture as the company scales.

Train employees on enterprise AI tools, build reusable Claude skills, prompt templates, and agentic workflows, and connect systems through MCP connectors. Pilot and scale AI solutions, manage training and knowledge libraries, support an AI champions network, measure adoption, and enforce AI governance, privacy, security, and access policies.