Top AI & Machine Learning Jobs

Machine Learning Engineer Interns will develop, deploy, and operate production-scale ML models and pipelines. They will lead an end-to-end research-to-production project, apply modern ML techniques to blockchain and crypto use cases, collaborate with senior engineers and product teams, and present findings to stakeholders. The role requires doctoral-level machine learning research, model development experience with PyTorch or TensorFlow, production-quality Python skills, and software engineering fundamentals.

Data Science Interns collaborate with product and engineering teams to analyze data, build models, support A/B tests and causal inference studies, maintain analytical datasets and pipelines, and communicate actionable insights. The role involves solving complex business problems with senior data scientists and applying responsible generative AI to improve workflow efficiency, cost, and quality. This is a 12-week summer 2027 internship with quarterly in-person work sessions.

Architect and build production agentic AI systems for customer support and compliance automation. Lead technical direction for distributed systems, APIs, orchestration frameworks, guardrails, and evaluation systems. Create reusable automation primitives, improve grounding and reliability, reduce hallucinations, and mentor engineers on LLM application design and production readiness. Collaborate cross-functionally to scale AI automation across Coinbase.

Design and implement a unified orchestration layer coordinating vendor and internal LLMs and human agents. Build production-grade Python services, drive ML projects from design to delivery, ensure secure, scalable AI-enabled customer experiences, and collaborate across teams to meet security, compliance, and performance standards.

Drive data science for Coinbase consumer products by analyzing ambiguous business problems, defining metrics, building experimentation and causal frameworks, developing advanced models, maintaining ETL pipelines, and translating insights into product strategy. The role requires SQL and Python expertise, statistical rigor, data visualization, stakeholder influence, code reviews, and responsible use of generative AI tools.

Lead end-to-end pricing experimentation and strategy for consumer and business products. Design and analyze A/B tests, build pricing models and production-grade data pipelines, partner cross-functionally, and present actionable recommendations to senior leadership.

Lead end-to-end ML systems for identity verification, including document authenticity, face match, liveness and deepfake detection. Build GNN-based identity graphs, behavioral/device-intelligence models, vendor benchmarking/evaluation, and production enforcement. Mentor engineers and align ML architecture with Product, Compliance, Risk, and Security stakeholders.

Lead engineering of an AI-native finance automation platform: design LLM-based agents and multi-agent orchestration, integrate with NetSuite/Workday/Anaplan, ensure SOX-compliant traceability and human-in-the-loop controls, and partner with FP&A/Treasury to translate spreadsheet workflows into production systems.

Design and deliver LLM-powered, agentic AI systems to automate customer support and compliance. Build scalable, secure backend infrastructure in Python and Go, integrate third-party models/APIs, implement grounding and guardrails, and measure performance. Lead end-to-end projects, mentor engineers, conduct code reviews, and participate in on-call and incident response to improve resolution times and reduce operational costs.

Design and build machine learning systems for Coinbase, responsibly use generative AI tools and copilots, apply human-in-the-loop practices, and deliver measurable efficiency, cost, and quality improvements while collaborating in a remote-first environment with periodic in-person surges.