Top AI & Machine Learning Jobs

Develop machine learning, AI, statistical, and predictive models using Snowflake and other data sources. Perform data extraction, cleansing, feature engineering, experimentation, visualization, and quality analysis. Partner with business and technical teams to deliver data products, conversational analytics, and actionable recommendations. Lead discovery sessions, communicate insights to nontechnical audiences, mentor analysts, and support enterprise data science projects.

The role involves managing the AI product lifecycle, building LLM-based workflows, defining product requirements, monitoring performance, and collaborating with teams to ensure effective AI solutions.

Leads Revance’s enterprise intelligence transformation, owning data architecture, semantic governance, AI and ML platforms, commercial intelligence, and actionable reporting. Builds and leads data engineering, data science, and analytics teams; establishes DataOps, MLOps, governance, quality, lineage, observability, and AI guardrails. Integrates intelligence into CRM, ordering, loyalty, and digital workflows to improve revenue, conversion, retention, and customer value. Requires executive leadership, scalable platform architecture, recommendation systems, and significant data and AI experience.

Build, deploy, and operate production AI applications and scalable machine learning solutions. Develop automated training and deployment workflows, support production AI infrastructure, and collaborate with software, data science, and platform engineering teams. The role involves Python development, ML frameworks, containerization, Kubernetes, Linux, and Git, with preferred experience in LLMs, RAG pipelines, model serving, vector databases, frontend development, and ML orchestration.

Lead multimodal research strategy for video and sensor understanding at petabyte scale. Select models, representations, training approaches, and evaluation methods; transition prototypes into production inference; define benchmarks; and optimize distillation, routing, cascades, quantization, GPU utilization, throughput, and cost. Translate customer research needs into technical programs while remaining hands-on with papers, models, experiments, and architecture. This is a senior individual contributor role with no direct reports.

Develops, trains, optimizes, and deploys computer vision and deep learning solutions for image and video analysis, including object detection, segmentation, sensor fusion, and pose estimation. Designs end-to-end architectures and real-time embedded or edge deployments, supports product development from proof of concept through validation, and collaborates with technical and management teams on product strategy, integration, and technical oversight.

Designs and builds high-performance data infrastructure for machine learning model training and feature serving. Responsibilities include developing distributed processing architectures, optimizing training data pipelines, resolving performance bottlenecks, collaborating with ML research teams, and establishing tooling and best practices for ML data infrastructure. The role requires experience with fault-tolerant distributed systems and frameworks such as Apache Spark or Flink.

Lead executive discovery, enterprise AI deployments, and production rollouts across cloud, on-premises, and firewalled environments. Build MCP-powered context layers, resolve SSO and networking challenges, expand customer use cases, shape go-to-market messaging, and feed customer insights into product development. The role requires hands-on LLM and agent experience, strong commercial judgment, technical breadth, and frequent customer-site travel.

Leads Curri’s end-to-end data and AI strategy, including analytics, machine learning, data platforms, governance, and product intelligence. Owns the roadmap for routing, forecasting, pricing, and delivery optimization; moves models into production; partners with product and engineering; and builds a high-performing data and ML team. The role also oversees Snowflake-based data foundations, business metrics, decision support, and data quality and reliability.

Build and operate AI-native, full-stack products for sales, marketing, and revenue operations teams. Develop agent workflows, automations, integrations, and production applications for research, enrichment, account scoring, and other go-to-market needs. Collaborate with nontechnical users to understand workflows, test solutions, and improve products. Integrate systems such as Salesforce, Marketo, and Gong while making pragmatic build-versus-buy decisions.