Top Tech Jobs & Startup Jobs in NYC, NY

Conduct applied AI research and build foundation models through design, training, evaluation, validation, and implementation. Collaborate with data scientists, engineers, and product managers to deliver scalable AI products. Research LLM pretraining, fine-tuning, reinforcement learning, robustness, explainability, and behavioral models. Develop platform or solution-level code, reproduce research, document experiments, and contribute to technical reports or conference submissions.

Conduct applied research in AI foundations, large language models, and agentic AI. Build and optimize foundation models through design, training, evaluation, validation, and deployment. Collaborate with data scientists, engineers, and product managers to deliver scalable AI products, translate research into business outcomes, and publish technical findings. Requires advanced research expertise in deep learning, LLMs, optimization, fine-tuning, data preparation, and reproducible experimentation.

Designs, develops, deploys, and supports foundational AI systems, including LLM inference, agentic workflows, vector search, model evaluation, governance, and observability. Optimizes large-scale AI systems for cost, latency, throughput, scalability, and hardware utilization. Leads architecture and design reviews, establishes AI engineering standards, drives technical roadmaps, and mentors senior AI engineers.

Draft, redline, negotiate, and manage third-party contracts for enterprise supplier relationships. Partner with procurement, legal, compliance, and business stakeholders to mitigate risk, drive transactions to completion, and ensure contracts meet Capital One standards.

Leads Bank Tech workforce strategy, associate experience, operational excellence, and organizational scaling. Advises senior technology leaders, develops labor and capacity plans, establishes management rhythms and governance, translates strategic goals into execution, and partners with Finance, HR, Labor, engineering, and product teams. The role requires executive-ready communication, complex analysis, cross-functional leadership, and extensive people and program management experience.

Lead strategic product initiatives for Consumer and Developer Experience (CDX), perform consulting-style analysis and analytics, drive AI transformation workstreams, set long-term product vision, develop prototypes with engineering, and build cross-functional stakeholder relationships to align and execute strategic priorities.

Own and prioritize product roadmaps for Capital One’s Card Core platform. Collaborate with stakeholders, technology teams, designers, data scientists, and subject-matter experts to define requirements, break down epics into features and user stories, monitor product health and risk metrics, and deliver customer-focused digital experiences. Support AI-powered onboarding features, apply agile product management practices, and communicate roadmap progress while ensuring quality, documentation, and business value.

Leads an AI engineering team building scalable, responsible generative AI platform services, including foundation model training, LLM inference, similarity search, guardrails, evaluation, observability, and governance. Establishes technical strategy, optimizes production AI systems for cost and performance, manages cloud deployments, translates enterprise goals into roadmaps, and develops engineering talent.

Design, develop, optimize, deploy, and support foundational AI systems, including foundation model training, LLM inference, agentic workflows, vector search, guardrails, evaluation, governance, and observability. Lead optimization of production AI systems for scalability, latency, throughput, hardware utilization, and cost. Architect multi-model orchestration pipelines, establish governance standards, lead design reviews, contribute to technical roadmaps, and mentor senior AI engineering staff.

Build and deploy responsible AI systems, including foundation model training, LLM inference, agents, similarity search, guardrails, evaluation, and observability. Optimize AI infrastructure for scalability, latency, throughput, cost, and hardware utilization. Develop deployment pipelines, testing environments, telemetry, prompts, evaluation datasets, and regression tests while contributing to foundational AI roadmaps and engineering standards.