Top AI & Machine Learning Jobs

Conduct applied AI research focused on recommendation systems and foundation models. Design, train, evaluate, validate, and implement large-scale models using PyTorch, AWS, Hugging Face, and Lightning. Collaborate with data scientists, engineers, and product managers to deliver AI products, translate research into business outcomes, and publish technical findings. Own research agendas, reproduce and extend peer-reviewed work, and optimize models for large-scale training and inference.

Design, develop, deploy, and support foundational AI systems, including LLM inference, agentic workflows, vector search, guardrails, evaluation, orchestration, and observability. Optimize model performance, scalability, latency, throughput, hardware utilization, and cost. Establish AI governance standards, lead technical reviews, shape long-term platform strategy, and mentor senior AI engineers. Collaborate with engineering, research, product, and program teams to deliver responsible AI products.

Design, develop, deploy, and optimize large-scale AI systems, including foundation models, LLM inference, agentic workflows, vector search, guardrails, and model evaluation. Lead AI architecture, orchestration, cost-performance governance, technical reviews, roadmap development, and responsible AI standards. Mentor senior technical staff and apply advanced optimization techniques to improve model scalability, latency, throughput, hardware utilization, and inference cost.

Build, deploy, optimize, and monitor production machine learning models and infrastructure. Develop ML applications, data pipelines, cloud architectures, and distributed systems using technologies such as PyTorch, TensorFlow, Spark, Ray, and Kubernetes. Collaborate with Product and Data Science teams, automate testing and deployment, maintain model governance, and apply responsible and explainable AI practices.

Leads data science strategy for Consumer and Developer Experience, partnering with product, engineering, and business teams. Builds and deploys machine learning models across large numeric and textual datasets, translating complex analysis into business outcomes. Responsibilities include statistical modeling, model validation, experimentation, prototyping, innovation, stakeholder communication, and team talent development using Python, cloud platforms, and open-source technologies.

Design, develop, deploy, and support foundational AI systems, including LLM inference, agentic workflows, vector search, model evaluation, governance, and observability. Optimize large-scale AI systems for cost, latency, throughput, scalability, and hardware utilization. Architect multi-model orchestration pipelines, establish AI engineering standards, lead technical reviews, and mentor senior engineering leaders while applying emerging research responsibly in production.

Conduct applied AI research and build foundation models across design, training, evaluation, validation, and production implementation. Collaborate with data scientists, software and machine learning engineers, and product managers to deliver scalable AI products. Research LLMs, optimization, fine-tuning, and agentic AI; publish findings through technical reports and conference submissions; and independently pursue impactful research agendas.

Conduct applied AI research and build foundation models from design through training, evaluation, validation, and production implementation. Collaborate with data scientists, engineers, and product managers to deliver AI-powered products at scale. Research emerging methods, optimize large deep learning models, translate technical findings into business outcomes, and contribute to technical reports and conference publications. The role requires advanced expertise in foundation models, deep learning, optimization, NLP, or fine-tuning.

Leads applied AI research and teams developing foundation models and production-scale machine learning systems. Sets research strategy, mentors applied scientists, partners with engineering and product leaders, and translates advanced AI research into customer-facing products. Requires expertise in large-scale deep learning, LLMs, optimization, model deployment, research publications, and organizational leadership.

Leads data science efforts for foundational AI and automation across a global payments network. Partners with data scientists, engineers, and product managers to develop scalable machine learning engines, AI agents, merchant intelligence, and risk solutions. Serves as an NLP and LLM domain expert, fine-tuning and deploying models using PyTorch, AWS, vector databases, and modern AI technologies. Translates complex ML work into business value while mentoring teams and delivering production-scale platforms and solutions.