Top Tech Jobs & Startup Jobs in NYC, NY

Designs, develops, deploys, and supports foundational AI systems, including LLM inference, foundation model training, agentic workflows, similarity search, guardrails, evaluation, governance, and observability. Optimizes AI systems for cost, latency, throughput, scalability, and hardware utilization. Owns complex AI architecture, defines reliability objectives, leads technical reviews, ensures security and compliance, contributes to long-term technical roadmaps, and mentors senior and principal engineers.

Develop and deploy large-scale machine learning and personalization models for web and mobile customer experiences. Partner with data scientists, engineers, and product managers; analyze massive numeric and textual datasets; and manage model design, training, evaluation, validation, and implementation. Research advanced methods including foundation models, reinforcement learning, causal inference, transformer architectures, and recommender systems while translating technical findings into business outcomes.

Conduct applied research in AI foundations, large language models, and agentic AI. Build and evaluate foundation models, optimize training and inference, develop scalable AI solutions, and translate research into production systems. Lead cross-functional research initiatives with data scientists, software engineers, ML engineers, and product managers while pursuing impactful research and mentoring junior team members.

Lead Capital One’s customer management platform for credit line increases, decreases, and balance transfers. Define the next-generation platform vision, oversee high-impact product delivery, manage audit and customer readiness, and lead 4–6 product team members. Partner with technology teams, translate business strategy into digital products, coach product managers, and drive customer-focused, technology-enabled transformation.

Lead product management for enterprise data platforms, aligning cross-functional teams to build a unified data destination. Drive data governance, trust, discovery, and scalable data products using modern platforms to enable real-time, AI-driven experiences and expand use of external data.

Leads and mentors product managers building scalable customer restrictions and data products. Defines product and partner roadmaps, drives adoption of governed data platforms, and delivers real-time capabilities that improve customer experiences, prevent fraud, and accelerate access to funds. Partners with technology, data, analytics, and business teams to solve complex problems, manage product risks, and align enterprise-wide capabilities.

Develop and deploy high-scale machine learning and personalization models for Capital One’s web and mobile experiences. Responsibilities include researching recommendation systems, foundation models, reinforcement learning, causal inference, and transformer architectures; analyzing large datasets; building models through training, evaluation, validation, and implementation; and collaborating with data scientists, engineers, and product managers.

Lead a greenfield enterprise product initiative for third-party management across Capital One’s supplier network. Drive product strategy, customer-centered solutions, technology delivery, risk mitigation, cross-functional alignment, and transformational outcomes for Enterprise Services & Supplier Product, Risk & Operations.

Build and optimize foundational AI systems, including LLM training and inference, agentic workflows, vector search, guardrails, evaluation, and observability. Partner with engineering, research, product, and program teams to deliver scalable AI products. Lead model and infrastructure optimization for cost, latency, throughput, and hardware utilization; establish governance and engineering standards; contribute to technical roadmaps; and mentor senior AI leaders.

Design, develop, deploy, and support foundational AI systems, including foundation-model training, LLM inference, agentic workflows, similarity search, guardrails, evaluation, governance, and observability. Optimize distributed AI systems for cost, latency, throughput, scalability, and hardware utilization. Own complex architectures, define reliability objectives, lead technical reviews, ensure security and compliance, contribute to long-term AI roadmaps, and mentor engineering teams.