Top Tech Jobs & Startup Jobs in NYC, NY

Own the vision, strategy, and execution for Account Opening Decisioning detection capabilities. Lead the full detection lifecycle, using data and model strategies to combat fraud while enabling seamless experiences for low-risk applicants. Partner with Data Science, Engineering, enterprise teams, and product stakeholders to develop consumer-facing digital products, solve complex problems, manage product risks, and drive cross-functional alignment.

Design, develop, deploy, and support foundational AI systems, including model training, LLM inference, agentic workflows, similarity search, guardrails, evaluation, governance, and observability. Optimize large-scale AI systems for cost, latency, throughput, scalability, and hardware utilization. Own complex system architecture, define reliability objectives, lead technical reviews, ensure security and compliance, contribute to strategic roadmaps, and mentor senior technical staff.

Lead the Selection Experiences product portfolio, overseeing Product Managers who develop assessments, screening, and interview solutions for Capital One’s talent acquisition journey. Build customer-focused digital products, translate business strategy into outcomes, drive technology-enabled transformation, and align cross-functional stakeholders across recruiting and hiring functions.

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 performance governance, lead design reviews, define technical roadmaps, and mentor senior AI engineering talent.

Leads data science for AI Foundations, developing and deploying scalable NLP, machine learning, and generative AI solutions. Partners with data scientists, engineers, and product managers to train, evaluate, validate, and operationalize models serving millions of customers. Applies expertise in large language models, optimization, self-supervised learning, explainability, or RLHF while mentoring talent and translating technical work into business outcomes.

Lead development of scalable AI and machine learning solutions for customer-facing applications. Build, fine-tune, evaluate, and validate NLP models and large language models, partnering with engineering, product, and design teams to operationalize them at scale. Research emerging generative AI technologies, deliver production-ready libraries and solutions, translate technical findings into business outcomes, and provide technical leadership across cross-functional teams.

Leads Capital One’s Developer Experience Telemetry workstream, defining strategy and guardrails for collecting software development data. Builds unified data products, developer tools, usage dashboards, and cost-optimization mechanisms while balancing privacy, security, compliance, and risk. Establishes rule-based and model-based evaluation frameworks, measures developer productivity and ROI, and drives data-informed decisioning. Partners with Engineering, Machine Learning, Cyber, and Enterprise Data teams while influencing senior executives and leading cross-functional product initiatives.

Drives project tracking and partner onboarding for the Customer Identity platform. Coordinates milestones across intake, development, testing, and launch; manages status updates, metrics, dashboards, and operating models; advises business partners on identity solutions; and collaborates with product, architecture, and engineering teams on implementation, release planning, and sprint tracking.

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.

Own the strategy, development, and lifecycle of scalable data products supporting analytics and machine learning. Partner with technology, business, and risk teams to translate needs into requirements, guide agile delivery, improve data quality and reliability, and enable self-service insights through usable, high-performing datasets and visualizations.