Top AI & Machine Learning Jobs

Research and prototype transformer-based and graph-based foundation models using customer, transaction, and merchant data. The intern will design and train models, run rigorous experiments, evaluate results, and develop working prototypes. Responsibilities include collaborating with Product, Engineering, Analytics, and Applied Science teams to improve customer experience, operational efficiency, and business outcomes.

Conduct applied AI research focused on computer vision, multimodal, robotics, and coding models. Design datasets, evaluations, benchmarks, and reinforcement learning environments; analyze model failures; develop data remediation specifications; validate data quality through fine-tuning and ablation studies; contribute to open models; publish research; and collaborate with frontier labs to translate model roadmaps into data programs.

Lead the architecture and development of backend platforms and agentic AI systems. Design scalable distributed services, APIs, runtimes, orchestration, integrations, evaluation systems, and observability capabilities. Drive ambiguous initiatives from concept through production, ensure reliability and safety, collaborate with customers and product teams, and mentor engineers while remaining hands-on with coding and operations.

Design and architect scalable AI-powered software systems, make technical decisions, ship production code, optimize performance, and resolve technical roadblocks. Partner cross-functionally to deliver initiatives from concept through completion, champion AI-forward development workflows, and contribute to evolving platform and product architecture in a highly regulated healthcare environment.

Architects, develops, and deploys high-performance machine learning systems across the full lifecycle. Responsibilities include large-scale feature engineering, online and offline pipelines, tree-based and deep learning models, low-latency serving, MLOps automation, model monitoring, and rigorous experimentation. The role requires production-grade software engineering, distributed computing, statistical expertise, and experience shipping ML systems in high-scale environments.

Build and deploy production GenAI solutions on Google Cloud, including agentic assistants, RAG pipelines, model integrations, API tools, and multi-agent systems. Evaluate and tune models, develop LLM testing and regression harnesses, ensure solution quality, and convert client-specific implementations into reusable assets. Collaborate with data engineering, AgentOps, forward-deployed engineering, and engagement teams to deliver secure, reliable solutions for higher education and SLED clients.

Build and operationalize production AI solutions for federal clients, from stakeholder discovery and architecture through evaluation, deployment, governance, monitoring, and optimization. Develop LLM applications using retrieval, structured outputs, tool use, and agentic workflows. Define quality metrics, create evaluation sets, document limitations, advise clients on AI use cases, and mentor engineers while contributing to reusable internal patterns and products.

Drive firm-wide adoption of generative AI by embedding with teams, providing user support, delivering in-person and virtual training, creating practical guidance, evaluating emerging tools, gathering feedback, and informing rollout decisions. The role translates technical AI capabilities into business workflows, supports senior stakeholders, troubleshoots common issues, and travels internationally as needed to deliver training and adoption support.

Lead the AI product vision and roadmap across corporate solutions, translating LLMs and ML into SaaS features. Partner with product, engineering, and data science teams to prioritize, develop, pilot, and commercialize AI-native and AI-enabled capabilities for governance, compensation, sustainability, and cyber risk.

Embed with grocery customers to scope, integrate, and deploy production LLM- and agent-powered systems. Build and harden platform components (knowledge graph/ontology, retrieval, agent frameworks, serving, evals, and observability) so field learnings become reusable tooling. Balance customer-facing implementation with platform engineering to enable scalable, reliable AI in production.