Top AI & Machine Learning Jobs

Own the quality, safety, performance, and evaluation of AI-powered consumer features. Build prompt and model evaluation frameworks, regression suites, guardrails, PII redaction, prompt-injection defenses, and compliance controls. Develop analyst and QA tooling, optimize cost and latency, lead model upgrades and A/B tests, and evaluate fairness and bias. Partner with engineering, analytics, legal, compliance, and product stakeholders while contributing to Python and Java services in production.

Leads KIPP Foundation’s network-wide AI strategy, pilots, evaluation framework, vendor recommendations, and regional coordination. Advises senior leaders on instructional and operational AI, assesses efficacy, accessibility, equity, privacy, security, and cost, and translates findings into scale decisions. Builds partnerships with vendors, researchers, universities, funders, and KIPP regions while shaping national guidance and external communications. Requires deep AI expertise, substantial K-12 experience, evaluation leadership, and knowledge of FERPA and student data privacy.

Build and maintain production pipelines for AI model evaluation, scoring, monitoring, and optimization. Develop embeddings, classifiers, scoring services, dashboards, and internal tools for agent observability and safety. Apply fine-tuning and model adaptation techniques, run structured experiments, assess performance tradeoffs, and collaborate with engineering and product teams to ship reliable AI capabilities.

Designs and implements AI and automation solutions for HR by translating business needs into technical requirements, user journeys, prototypes, test plans, and scalable experiences. Leads UAT, documentation, performance monitoring, troubleshooting, and continuous improvement while partnering with HR stakeholders, technology teams, architects, developers, data scientists, and vendors. The onsite Minneapolis role requires up to 10% travel and experience with enterprise AI, automation, or chatbot implementations.

Build and maintain production computer vision preprocessing pipelines for virtual try-on, including segmentation, pose estimation, human parsing, and body mapping. Validate inputs, benchmark and integrate vision models, evaluate performance on internal datasets, perform data quality checks, and collaborate with ML scientists to productionize research.

Build and operate AI-native full-stack products, including multi-agent LLM orchestration, scalable FastAPI services, data pipelines, React and Next.js interfaces, experimentation platforms, evaluation systems, and reliability infrastructure. The role owns features from design through deployment, optimizes performance, leads incident response, partners with customers and cross-functional teams, and mentors engineers. It requires strong Python and TypeScript skills, experience with LLM systems, event-driven workflows, observability, enterprise security, and rapid product delivery.

Leads Hazen’s enterprise AI strategy, roadmap, governance, adoption, and implementation across engineering and corporate functions. Builds and manages an AI team, prioritizes use cases, oversees enterprise platforms and custom AI solutions, establishes responsible-use standards, and partners with IT, cybersecurity, Legal, and business leaders. Drives training and change management while ensuring data security, professional accountability, and regulatory compliance.

Leads AI projects from discovery through implementation, evaluation, and maintenance. Builds secure, production-ready AI workflows integrated with organizational data, applications, APIs, repositories, cloud platforms, and business systems. Establishes standards for AI architecture, governance, monitoring, permissions, guardrails, and responsible use. Advises leadership on models, vendors, risks, and adoption strategies; supports users through technical guidance and training; and mentors junior software engineers.

Owns the architecture and safe operation of high-volume autonomous AI development pipelines. Responsibilities include generator-verifier systems, LLM agent orchestration, sandboxing, CI/CD, evaluation suites, security boundaries, AWS Bedrock inference, compliance guardrails, staged deployment, and human review standards. The role establishes engineering practices for multiple delivery teams and serves as the final accountability gate before agent-generated code reaches production.

Serves as a hands-on technical lead building, deploying, and operating governed agentic AI and LLM solutions on Databricks. Develops RAG and multi-agent systems, MCP integrations, secure tools, prompt and context engineering, AI Gateway controls, identity authorization, observability, CI/CD, testing, and monitoring. Partners with business, security, risk, compliance, and data teams to deliver measurable outcomes in a regulated banking environment, while establishing governance standards and mentoring engineers.