Top Tech Jobs & Startup Jobs in NYC, NY

Leads multiple technology projects and engineering teams while designing and implementing large-scale, distributed, cloud-native systems. Provides technical leadership across teams, shapes architecture and platform roadmaps, mentors engineers, drives reliability and performance, and partners with product leaders. Uses modern programming languages, cloud platforms, microservices, databases, containers, CI/CD, observability, and AI-based development tools to deliver secure solutions supporting regulatory and customer needs.

Leads full-stack engineering projects and developers building distributed microservices and cloud-based systems. Provides technical direction across Agile teams, defines service boundaries and integration patterns, reduces technical debt, improves system performance, and establishes engineering standards. Collaborates with product managers and architects while using modern programming languages, databases, containers, AWS services, AI coding tools, CI/CD, and observability technologies.

Conduct applied AI research and develop foundation models from design through production deployment. Collaborate with data scientists, software and machine learning engineers, and product managers to build scalable AI products. Research deep learning, language, graph, sequential, and behavioral models; optimize training and inference; reproduce emerging research; deliver platform or solution code; and publish internal or external technical findings.

Design, develop, deploy, and support production AI systems, including foundation model training, LLM inference, agentic workflows, vector search, guardrails, evaluation, governance, and observability. Optimize model performance, scalability, latency, throughput, hardware utilization, and cost. Architect multi-model orchestration pipelines, lead technical governance and design reviews, define AI engineering standards, and mentor senior technical staff.

Conduct applied AI research and build foundation models from design through production deployment. Partner with data scientists, software engineers, machine learning engineers, and product managers to develop scalable AI products. Lead research initiatives, optimize training and inference, evaluate emerging methods, translate research into business outcomes, and deliver model libraries or platform code. Work may focus on large language models, deep learning optimization, fine-tuning, robustness, explainability, or reinforcement learning.

Design, develop, deploy, and optimize foundational AI systems, including LLM inference, agentic workflows, vector search, guardrails, model evaluation, and orchestration pipelines. Lead cost-performance governance, technical design reviews, AI standards, roadmap development, and mentoring. Apply advanced optimization techniques to improve scalability, latency, throughput, hardware utilization, and inference costs while ensuring responsible AI deployment.

Designs, develops, deploys, and supports large-scale AI systems, including foundation-model training, LLM inference, agentic workflows, similarity search, guardrails, evaluation, governance, and observability. The role optimizes AI infrastructure for scalability, latency, throughput, cost, and hardware utilization; defines reliability objectives; leads architecture and technical reviews; and mentors engineering staff.

Design, develop, deploy, and optimize foundational AI systems, including large language model inference, foundation model training, agentic workflows, vector search, guardrails, evaluation, governance, and observability. Lead architecture, cost-performance optimization, technical standards, and design reviews for scalable production AI systems. Mentor senior technical staff and contribute to the long-term roadmap for responsible AI infrastructure.

Provides second-line compliance advisory and effective challenge for Capital One’s network and merchant businesses. The role develops network compliance expertise, assesses laws and regulations, identifies emerging payments risks, supports new products and business expansions, and collaborates with Business Risk, Legal, Audit, and senior leaders to strengthen risk management frameworks.

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 scalable AI products. Research areas include large language models, deep learning, optimization, self-supervised learning, robustness, explainability, reinforcement learning from human feedback, and model fine-tuning. Develop research libraries and platform code, publish findings, reproduce peer-reviewed work, and independently pursue impactful research agendas.