Top AI & Machine Learning Jobs

Builds and enables responsible AI solutions across the organization. Responsibilities include managing AI use-case intake, coaching teams, developing Copilot Studio agents and Power Platform automations, supporting AI-powered developer tools, establishing governance and repeatable patterns, measuring adoption, and delivering training. The role requires strong communication, software development or architecture experience, Microsoft Copilot Studio expertise, and Microsoft 365 administration experience.

Support the design, development, testing, and deployment of AI-powered applications across front-end, back-end, and data workflows. Collaborate with engineers, researchers, product managers, and operators to translate customer needs into product requirements, integrate AI models, evaluate outputs, improve reliability and usability, and ship production-grade tools for government, healthcare, and industrial customers.

Drive performance improvements for an AI advertising product through large-scale data analysis, metric development, system health evaluation, experimentation, and algorithm improvement. Analyze ad auction and user interaction data, diagnose bidding and machine learning systems, define success metrics, and collaborate with product, engineering, and applied science teams to guide product decisions and improve online prediction reliability, latency, and efficiency.

Define platform architecture, build agent runtimes and evaluation harnesses, scale AI training and inference pipelines, and deploy Fastino into customer-managed AWS environments. Develop secure sandboxed execution for agent workloads, ship full-stack features, partner with AI researchers, mentor engineers, guide AI coding agents, and translate customer feedback into product decisions.

Conduct a focused research project on agentic AI systems for applications such as robotics, vehicle design, factory logistics, or numerical optimization. Define project scope with a mentor, develop and evaluate AI methods, and work toward publication at a relevant research venue. The role requires foundation-model fine-tuning with reinforcement learning and use of agentic workflows to accelerate research experiments.

Develop and optimize computer vision and machine learning perception algorithms for autonomous detection, classification, and pose estimation of space targets. Responsibilities include model training, evaluation, deployment to resource-constrained systems, simulation integration, sensor modeling, tooling maintenance, and collaboration across autonomy and mission software teams.

Designs, builds, and operates AWS-based data lakes and GenAI platforms for safety and mission assurance. Develops heterogeneous data pipelines, RAG and agentic workflows, APIs and MCP servers, identity integrations, evaluation and observability systems, and governance controls. Integrates AI capabilities with enterprise applications and Microsoft 365 while ensuring security, traceability, responsible AI, and regulatory compliance. Requires collaboration with technical and mission stakeholders and residence in the Greater Cleveland Area.

Develop machine learning and AI tools for Formula 1 engineering performance analysis. Responsibilities include analyzing imagery and simulation data, detecting trends and anomalies, building embeddings and similarity models, applying LLMs and retrieval-augmented systems, and creating internal engineering tools. The placement involves collaborating with domain experts and deploying prototype systems using modern software frameworks.

Develop machine learning models and advanced analytics solutions across the full model lifecycle, including data exploration, feature engineering, validation, refinement, and operational monitoring. Build AI capabilities for intelligent products and data-driven decisions while monitoring system health, user metrics, performance, and availability. Collaborate across teams, mentor technical staff, optimize processes, and apply secure software development practices.

Analyze operational, financial, HR, and job-management data; build dashboards, predictive models, and business forecasts; identify and implement AI and automation opportunities; integrate AI tools through APIs and automation platforms; measure productivity and KPI improvements; support tool adoption and training; and ensure responsible data and AI practices.