Top AI & Machine Learning Jobs

Lead enterprise delivery of Databricks-based data, machine learning, and Generative AI solutions. Design production data pipelines, analytics engines, RAG applications, and ML platforms; migrate legacy data estates to the Lakehouse; and advise executive client stakeholders. Drive platform adoption, identify expansion opportunities, mentor technical teams, and develop reusable engineering accelerators. The role combines hands-on architecture, engineering, consulting, customer engagement, and technical practice building.

Provide data analysis, graphics, and dashboards to identify challenges and opportunities. Use Python or R, SQL, relational and NoSQL databases, Tableau, and time-series analysis to transform unstructured data into actionable insights supporting customer decisions about production, resources, and personnel.

Own sales forecasting and revenue analytics by developing statistical and machine learning models across products, regions, and time horizons. Lead the full machine learning lifecycle from feature engineering through deployment, monitoring, and explainability. Translate forecasts into business recommendations, deliver production data products, mentor junior data scientists, and provide technical leadership across analytics and sales teams.

Build and productionize internal AI applications from business-unit prototypes. Responsibilities include auditing AI tools, refactoring applications for security and scalability, integrating LLMs with Salesforce and the enterprise data lake, maintaining deployed solutions, managing stakeholders, and enforcing data governance, access controls, and hallucination mitigation.

Designs, develops, and deploys agentic AI solutions and process automation for Samsung care operations. Responsibilities include multi-agent workflows, MCP servers, enterprise API integrations, databases and vector stores, low-code automation, cloud environments, CI/CD, monitoring, process analysis, and AI governance. The role leads use cases from proof of concept through production, collaborates with business stakeholders and Korea HQ, and communicates technical implementations to nontechnical audiences.

Lead data science measurement initiatives for media and advertising. Design experiments and causal models to measure incrementality, build marketing mix models, and productionize scalable data pipelines. Develop and deploy models using Python, R, SQL, and cloud platforms while ensuring data quality and privacy compliance. Deliver dashboards, automated reporting, and executive-ready insights, communicate findings to stakeholders, and mentor junior data scientists.

Full-time, 12-week data science internship contributing to business analytics projects. The intern will use SQL, Python, Power BI, and cloud analytics tools to build dashboards and analytical models, investigate data, collaborate with analytics teams and business leaders, and present project outcomes to senior leadership. The role also includes mentorship, professional development, networking, and exposure to organizational operations.

Optimize training and inference performance for structural and generative ML models. Develop custom CUDA and Triton kernels, use ML compilers, scale distributed training across multi-node GPU clusters, reduce inference costs, improve GPU utilization on GCP, and build benchmarking and profiling tools. Provide technical direction, select infrastructure, influence research teams, and mentor engineers.

Own AI projects from discovery through production, building and deploying AI-powered applications and workflows for GTM, operations, product, strategy, and partner teams. Translate ambiguous processes into scalable systems, integrate applications with the GTM technology stack, gather user requirements, prioritize projects, and coordinate cross-functional execution. Use structured data and context to improve AI reliability and track business performance.

Supports the development and deployment of production AI systems by fine-tuning and evaluating machine learning models, integrating AI features, working with training and evaluation data, running experiments, monitoring performance, and collaborating with engineering and product teams. Requires current enrollment in a relevant bachelor's or master's program, Python proficiency, familiarity with PyTorch or TensorFlow, and foundational machine learning knowledge.