Top Tech Jobs & Startup Jobs

Conduct applied research and build AI foundation models, particularly large language models and agentic AI systems, across design, training, evaluation, validation, and production implementation. Partner with data scientists, engineers, and product managers to deliver scalable AI products. Research optimization, fine-tuning, robustness, explainability, and related deep learning methods while translating technical findings into business outcomes and independently pursuing impactful research agendas.

Manages network documentation processes, including technical writing, content lifecycle management, workflow optimization, AI and automation integration, governance, risk management, and audit readiness. Serves as a subject matter expert, analyzes stakeholder requirements, leads documentation enhancements, influences cross-functional partners, and supports knowledge sharing, standard operating procedures, and team enablement.

Manages international technology, cybersecurity, and data risk advisory processes; coordinates country risk assessments, regulatory responses, audit preparation, and remediation challenges. Acts as liaison among technology risk, legal, compliance, and other stakeholder teams, translating complex technical and business requirements into compliant risk documentation. Supports regulatory inquiries, examinations, independent audits, and standardized responses across multiple jurisdictions.

The Lead AI Engineer will design, develop, and support AI software components, leveraging various AI technologies to enhance banking services. The role involves partnering with cross-functional teams to deliver AI-powered products and optimize large-scale production AI systems.

Lead end-to-end technical initiatives for enterprise web platforms, make cross-team architectural decisions, establish engineering standards, and promote AI-native development practices. Advise and influence engineering teams, collaborate with product and design leadership, mentor engineers, support interviewing and onboarding, and build high-traffic front-end experiences using JavaScript, Angular, React, Node.js, HTML, CSS, and TypeScript.

Leads the design, development, deployment, and production maintenance of machine learning systems at scale. Responsibilities include building and validating models, optimizing data pipelines, automating testing and deployment, monitoring production models, developing cloud-based ML architectures, applying CI/CD practices, and ensuring responsible, explainable, and secure AI. The role collaborates with Product, Data Science, and Agile teams and may involve leading ML engineering teams.

Leads multiple software engineering teams focused on developer experience and AI tooling. Responsibilities include hiring and mentoring engineers, partnering with product managers, improving engineering practices, designing and supporting full-stack solutions, and ensuring scalability, resilience, and availability. The role also leads AWS deployments, microservices modernization, Agile delivery, engineering intelligence initiatives, and adoption of emerging AI and developer productivity technologies.

Lead machine learning engineering for production systems at scale. Design and deploy models, data pipelines, cloud architectures, and ML infrastructure; write and test application code; automate deployment; monitor, retrain, and govern production models; and apply responsible AI practices. Collaborate with Product, Data Science, and Agile teams while using Python, Scala, or Java. The role also involves technical leadership and developing performant, resilient ML solutions.

Develop and enhance capital markets models, including derivatives and fixed-income models, while creating quantitative solutions for finance business problems. Apply statistical, econometric, and machine-learning methods to large datasets, improve processes through automation, and build cloud-based analytical solutions. Own model development from conceptualization through deployment, validation, training, and monitoring. Provide technical guidance, document models, and communicate analytical findings to diverse stakeholders.

Lead data science and machine learning initiatives for Capital One’s Global Payment Network. Build, validate, and implement models and AI tools supporting fraud, risk, merchant acceptance, intelligence, and monitoring. Collaborate with data scientists, engineers, and product managers while translating complex analytical work into business outcomes. Research emerging technologies, solve ambiguous problems, and develop team talent using Python, cloud platforms, open-source tools, and large-scale data.