Top Tech Jobs & Startup Jobs in Boston, MA

Leads applied AI research and strategic direction for foundation models and large-scale machine learning systems. Responsibilities include designing, training, evaluating, validating, and deploying models; advancing research in areas such as LLMs, optimization, self-supervised learning, robustness, and explainability; guiding applied scientists and managers; collaborating with engineering, product, and academic partners; and translating research into scalable customer-facing products.

Leads multiple technology projects and engineering teams building large-scale, cloud-native full-stack systems. Provides architectural and technical leadership, drives platform roadmaps, develops distributed microservices, mentors engineers, establishes coding standards, and improves system reliability and observability. The role partners with product leaders and architects to deliver secure, high-performance solutions using modern programming languages, cloud platforms, databases, containers, and development practices.

Conduct applied AI research focused on foundation models, LLM customization, and fine-tuning. Design, train, evaluate, validate, and deploy large-scale models using PyTorch, AWS, Hugging Face, and Lightning. Develop scalable research and platform code, reproduce and extend peer-reviewed work, design controlled experiments, publish findings, and collaborate with data scientists, engineers, and product managers to deliver AI-powered products.

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 full-stack software engineering teams, technology portfolios, and large-scale cloud-native initiatives. Responsibilities include setting technical strategy, managing engineering talent, driving architectural and roadmap decisions, delivering distributed microservices and full-stack systems, and aligning engineering execution with enterprise goals. The role involves AWS and other cloud technologies, modern programming languages, databases, containers, Agile delivery, AI-enabled development, and regulatory technology 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 complex, multi-year technical integration programs, including application convergence, data migration, platform standardization, and cloud-scale transformation. Establishes program roadmaps, operating mechanisms, maturity models, and measurable outcomes across engineering teams. Drives architectural alignment, dependency sequencing, risk escalation, and tradeoffs among cost, speed, and resiliency. Serves as an executive-level influencer and helps mature the organization’s technical program management discipline.

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.

Develop and deploy foundational AI systems, including foundation model training, LLM inference, agentic workflows, similarity search, guardrails, evaluation, governance, and observability. Optimize large-scale AI systems for cost, latency, throughput, scalability, and reliability. Own complex AI architecture, define reliability objectives, improve GPU/TPU utilization, lead technical reviews, ensure security and compliance, and mentor engineering colleagues.

Leads complex technical programs involving Generative AI, distributed systems, enterprise platforms, and cloud services. Establishes program roadmaps, operating mechanisms, maturity models, and measurable outcomes; coordinates architecture, dependencies, investment planning, and delivery across engineering teams. Acts as an executive escalation point, drives tradeoffs among speed, cost, and resiliency, and develops the organization’s TPM discipline through standards, tooling, metrics, and leadership influence.