Top Tech Jobs & Startup Jobs in NYC, NY

Leads full-stack engineering projects and developers building cloud-based, distributed microservices and cybersecurity solutions. Defines service boundaries, integration patterns, coding standards, and reusable frameworks; mentors engineers; evaluates emerging technologies; improves system performance and reduces technical debt. Works with product managers, architects, and Agile teams using modern programming languages, databases, containers, cloud platforms, CI/CD, infrastructure as code, automated testing, observability, and AI-assisted development tools.

Leads data scientists, business analysts, and data analysts in developing machine learning models from design through implementation. Partners with software engineering and product teams to deliver customer-focused products, using Python, cloud computing, Spark, H2O, and other technologies to analyze large-scale numeric and textual data. Translates complex technical findings into business goals while developing team talent and evaluating emerging methods.

Leads full-stack software engineering projects and developer teams, building distributed microservices and cloud-based systems. Responsibilities include designing APIs and reusable frameworks, mentoring engineers, establishing engineering standards, improving system performance, reducing technical debt, and shaping technology strategy. The role uses modern programming languages, databases, AWS services, Docker, Kubernetes, CI/CD, infrastructure as code, automated testing, observability, and AI-assisted development tools.

Manage FP&A for Capital One’s Enterprise Data organization, owning budgets, forecasts, monthly actuals, variance analysis, financial modeling, and business cases. Advise senior Enterprise Product and Technology leaders through executive-ready financial storytelling and recommendations. Lead process improvements, manage workflows, mentor junior team members, and partner cross-functionally to influence strategic decisions and financial outcomes.

Leads full-stack software engineering projects and developer teams building distributed, cloud-based microservices and systems. Designs service boundaries, APIs, reusable frameworks, and technical standards; mentors engineers; evaluates emerging technologies; improves performance and reduces technical debt. The role uses multiple programming languages, AWS and other cloud platforms, databases, containers, CI/CD, infrastructure as code, automated testing, observability, and modern AI coding tools.

Leads applied AI research and strategic direction for foundation models and large-scale machine learning systems. Partners with scientists, engineers, and product leaders to design, train, evaluate, validate, and deploy AI models. Defines long-term research priorities, advances state-of-the-art methods, mentors applied scientists and managers, collaborates with academia, and represents Capital One in the research community.

Leads data science for foundational AI and automation across a global payments network. Partners with data scientists, engineers, ML engineers, and product managers to develop scalable models and AI agents using PyTorch, AWS, vector databases, and LLMs. Serves as an NLP and LLM domain expert, translates technical work into business value, develops talent, and delivers production-scale libraries, platforms, and solutions.

Design, develop, deploy, and optimize production AI systems, including foundation model training, LLM inference, agentic workflows, vector search, guardrails, evaluation, governance, and observability. Lead multi-model orchestration and cost-performance optimization across cloud infrastructure. Establish AI engineering standards, guide technical roadmaps, lead design reviews, and mentor senior AI engineers while integrating research advancements into scalable, responsible products.

Leads data science initiatives using machine learning, statistical modeling, and large-scale data analysis. Partners with software engineers and product managers to develop customer-focused products, builds and validates models across the full development lifecycle, translates technical findings into business outcomes, evaluates emerging technologies, and develops data science talent. Requires substantial experience with programming, machine learning, relational databases, cloud computing, and people management.

Leads complex, multi-year technical integration programs, application convergence, data migration, and platform modernization initiatives. Establishes TPM operating models, roadmaps, technical investment plans, measurable platform outcomes, and delivery accountability across engineering teams. Serves as an executive escalation point, drives architectural alignment and tradeoffs, and expands TPM maturity through scalable processes, tooling, metrics, and organizational influence.