Top Tech Jobs & Startup Jobs

Leads a project management team responsible for well-managed controls and L2 process oversight across customer service platforms. Owns end-to-end processes, risk controls, operational resilience, stakeholder alignment, process automation, audit improvements, and strategic delivery execution. Partners with Risk, Product, Technology, and Data teams to improve efficiency, compliance, and platform reliability while coaching team members and driving scalable process innovation.

Lead strategic, data-informed communications programs for Premium Products and Experiences. Advise internal leaders, craft targeted messaging, manage cross-functional campaigns, mitigate reputational risk, measure outcomes, and oversee team operations to drive brand and business objectives.

Design, build, deploy, scale, and maintain production machine learning systems and models. Lead technical design, model and application development, data pipeline construction, cloud architecture, testing, CI/CD, monitoring, governance, and responsible AI practices. Collaborate with Product, Data Science, and Agile engineering teams while leading teams developing enterprise AI coding tools.

Lead software asset management execution, including license compliance reconciliations, Effective License Position analysis, software reclamation, entitlement and installation reviews, audit support, and asset data normalization. Maintain software catalogs, publisher and SKU records, resolve discovery and procurement discrepancies, and author operating procedures, process maps, workflows, and policies governing enterprise software management. The role also requires leadership experience and familiarity with DevOps tools, AWS, ServiceNow, and SAMPro.

Leads complex, high-priority projects and programs from planning through delivery. Provides strategic direction, manages scope, timelines, risks, governance, documentation, and cross-functional stakeholder alignment. Builds project frameworks, solves complex business problems, influences enterprise leaders, and mentors and develops project management staff.

Leads the design, development, deployment, and maintenance of machine learning systems at scale. Responsibilities include building ML models and data pipelines, writing production code, optimizing cloud architectures, automating testing and deployment, monitoring and retraining models, and ensuring performance, governance, security, responsible AI, and explainability. The role collaborates with Product and Data Science teams in an Agile environment focused on enterprise AI coding tools.

Partner with financial services leaders to analyze portfolio, customer, channel, and P&L trends; build financial models and sensitivities; translate complex data into executive-ready insights; support strategic decisions; manage complex analytical deliverables under regulatory deadlines; and mentor junior analysts. The role requires strong financial analysis, modeling, communication, problem-solving, stakeholder management, and data visualization capabilities.

Performs strategic and quantitative analysis supporting enterprise compliance testing, complaint programs, and customer experience improvements. Builds and tests models, develops recommendations, assesses operational feasibility, leads implementation, and creates monitoring plans. Partners with compliance, business, technology, product, legal, risk, and management leaders to ensure products remain compliant and customer-focused.

Conduct applied AI research and build foundation models through design, training, evaluation, validation, and production implementation. Collaborate with data scientists, software and machine learning engineers, and product managers to deliver scalable AI products. Research large deep learning models, optimization, self-supervised learning, robustness, explainability, and fine-tuning, while translating technical findings into business outcomes and pursuing an independent research agenda.

Build and support production AI platform components including foundation model training, LLM inference, similarity search, guardrails, evaluation, governance, experimentation, and observability. Optimize large-scale AI systems for scalability, latency, throughput, cost, and hardware utilization. Partner with engineering, research, product, and program teams; contribute to technical vision and roadmap; and lead or mentor engineering teams.