Top AI & Machine Learning Jobs

Develop statistical analyses, experiments, predictive models, and machine learning solutions for Sales and B2B Marketing. Analyze large transaction-level datasets, evaluate business performance and product features, deliver stakeholder reporting and recommendations, and take data science projects from development through production. The role requires strong SQL, Python or R, advanced modeling expertise, and familiarity with consumer finance, sales operations, and B2B marketing.

Lead a cross-functional team to develop medical coding software and improve a chart confidence pipeline. Translate stakeholder requirements, guide data scientists and engineers, evaluate recommendations, and report progress to internal stakeholders to increase automation and chart readiness.

Develop and deploy physical AI systems on real robots, including learned perception, vision-language-action models, imitation and reinforcement learning, world models, autonomous behavior, and sensor-data pipelines. Prototype approaches in simulation and field environments, optimize models for latency and edge deployment, and collaborate across robotics, controls, embedded, mechanical, and product engineering teams. The role also contributes to long-term Physical AI architecture and research direction.

Leads the AI product strategy and roadmap, extending governed Alteryx workflows and business logic into enterprise AI agents and LLM ecosystems. Owns the Faster, Better, Cheaper ROI narrative, benchmarks product performance, quantifies customer value, champions trustworthy AI through the VURA framework, and represents the product vision with customers, analysts, and external audiences.

Designs, builds, and operates AI-powered services within microservice and distributed architectures. Responsibilities include developing reliable AI agents, integrating tools and LLMs, debugging production issues across services and infrastructure, improving scalability and observability, and contributing to architectural decisions. The role requires strong Go development, experience with cloud infrastructure and Kubernetes, and effective use of AI coding agents.

Evaluate migration technologies, build quantitative models for migration risk and data quality, coordinate customer migration projects, develop data pipelines and analytical frameworks, implement Python and SQL validation workflows, assess cloud environments, and create dashboards and reporting for product, sales, and operations teams.

Designs and scales production-grade data and machine learning pipelines for fraud, payment risk, and abuse prevention. Responsibilities include ETL and feature engineering, model training and deployment, data and model monitoring, incident response, code reviews, mentoring, and collaboration with data scientists, engineers, and product managers. The role supports reliable, explainable decision-making across financial and operational systems.

Own the architecture, quality, and technical direction of a production perception system for home security hardware. Develop robust computer vision and machine learning pipelines for multi-camera tracking, sensor fusion, object identity, and edge GPU inference. Define evaluation metrics, regression infrastructure, observability, and failure handling while optimizing accuracy, latency, and cost. Lead cross-functional technical decisions and mentor engineers across perception, hardware, cloud, and product teams.

Own end-to-end technical engagements involving AI evaluations, embedded assessments, and rapid-response incident analysis. Scope projects, coordinate technical teams, manage dependencies, guide methodological decisions, maintain partner relationships, interpret evaluation results, and present findings to technical, governmental, media, and public audiences. The role also develops repeatable workflows and playbooks while collaborating with research and governance teams.

Build production AI agent systems for healthcare workflows such as scheduling and medication refills. Develop orchestration, tool-use, conversation-state, recovery, evaluation, and ML pipelines. Analyze production conversations, identify failures, create evaluation datasets, measure agent quality, and improve reliability across models and prompts. Partner with research to productionize techniques while balancing quality, latency, and cost.