Top Tech Jobs & Startup Jobs in NYC, NY

Conduct applied AI research and build foundation models across design, training, evaluation, validation, and deployment. Collaborate with data scientists, engineers, and product managers to deliver scalable AI products. Research advanced methods including deep learning, graph models, sequential models, optimization, robustness, explainability, and RLHF. Develop production-scale model libraries and solutions, publish findings, reproduce research, and pursue an independent research agenda.

Owns product strategy, user experience, and capabilities for Capital One’s enterprise API platform and governance initiatives. Leads cross-functional teams, translates customer and business needs into technology products, drives platform modernization and AI enablement, solves complex problems, manages product risks, and aligns stakeholders toward transformational outcomes.

Leads architecture and hands-on engineering for Capital One’s Business Travel products. Designs scalable, distributed, cloud-based systems; modernizes monoliths into microservices and event-driven platforms; establishes API and design standards; and drives technical vision across teams. The role also mentors engineers, leads architectural reviews, supports international expansion, and contributes to platform strategy, system quality, and engineering excellence.

Leads technology projects and developers building cloud-based, full-stack systems and distributed microservices. Provides technical direction across Agile teams, defines service boundaries and integration patterns, establishes engineering standards, reduces technical debt, improves performance, and influences technology strategy. The role involves mentoring engineers, collaborating with product managers and architects, and using modern programming languages, databases, containers, cloud platforms, APIs, CI/CD, infrastructure as code, and automated testing.

Design, develop, deploy, and support foundational AI systems, including foundation model training, LLM inference, agentic workflows, vector search, guardrails, 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 governance and design reviews, contribute to technical roadmaps, and mentor senior AI engineering leaders.

Lead a data science team developing scalable AI and machine learning products for Capital One’s mobile app. Build, fine-tune, evaluate, and operationalize NLP and large language models using technologies such as PyTorch, AWS, Hugging Face, and LangChain. Partner with engineering, product, and design teams to deliver customer-facing AI features, translate technical findings into business outcomes, and develop talent.

Own the vision, strategy, and roadmap for data modernization products supporting personalized experiences and business use cases. Collaborate with engineering, data, data science, design, and business teams to build scalable data ecosystems. Define acceptance criteria, break down epics into features and user stories, prioritize development, conduct hypothesis-driven analysis, manage risk, and communicate product roadmaps to stakeholders.

Leads a marketing data science team developing and deploying machine learning models for customer acquisition and personalization. Responsibilities include designing, training, validating, evaluating, and implementing models; analyzing large-scale structured and textual data; developing Python libraries; collaborating with engineers, product managers, campaign managers, and analysts; and translating technical insights into business outcomes. The role also includes team leadership and talent development in a hybrid work environment.

Leads strategic business analysis for Capital One Shopping, including growth and profitability strategies, product and pricing analytics, marketing insights, credit risk modeling, business requirements, and concurrent project execution. The role partners across technology, sales, operations, and finance, may manage analysts, and drives data-based decisions, product development, and organizational results.

Leads machine learning engineering teams while designing, deploying, scaling, monitoring, and governing production ML models and platforms. Responsibilities include building distributed ML systems, cloud architectures, data pipelines, CI/CD automation, model retraining, responsible AI practices, and resilient production services. The role combines hands-on technical leadership with mentoring, recruiting, talent development, and cross-functional collaboration with Product and Data Science teams.