Top Tech Jobs & Startup Jobs

Develop mathematical and computational methods to quantify information, uncertainty, redundancy, information loss, and the value of additional evidence in complex enterprise data. Apply information theory, Bayesian inference, statistics, and probabilistic modeling to build algorithms, prototypes, benchmarks, and evaluation methods for information-aware AI systems. Collaborate with research, engineering, and product teams to translate theoretical methods into practical systems.

Develop research methods for autonomous systems, including state and world modeling, estimation, planning, control, optimization, and sequential decision-making under uncertainty. Build algorithms, prototypes, benchmarks, evaluation methods, and product architectures, then collaborate with research, engineering, and product teams to deploy them in real systems.

Architect and improve enterprise AI platform availability, resilience, scalability, performance, and cost efficiency. Define measurable requirements, design failover and recovery mechanisms, lead capacity and performance planning, optimize AI infrastructure economics, eliminate bottlenecks, and drive production readiness across Engineering, Cloud Infrastructure, DevOps, QA, and AI teams.

Lead procurement strategy, vendor negotiations, and contract lifecycle management; run end-to-end RFP/RFI and complex deal processes; partner with product and engineering to design AI-enabled procurement solutions; support sales and client demos; manage vendor onboarding, client due diligence, and cross-functional initiatives to scale procurement capabilities.

Owns tax accuracy, quality, and performance for AI-powered Sales and Use Tax products. Translates complex indirect tax requirements into product specifications, testing strategies, evaluation frameworks, and roadmap priorities. Partners with Product, Engineering, and QA teams to deliver capabilities, investigate failures, and improve customer outcomes. Requires deep multi-state sales and use tax expertise, technology evaluation experience, strong stakeholder communication, and occasional travel.

Lead client-facing strategy and delivery for enterprise AI initiatives. Partner with customer stakeholders and technical teams to identify use cases, develop deployment roadmaps, evaluate value and feasibility, track KPIs, align stakeholders, and support rollout, adoption, and change management. The role requires structured problem-solving, business judgment, workshop facilitation, cross-functional coordination, and ownership of workstreams from discovery through deployment and value realization.

Leads enterprise AI deployment strategy and value realization across regulated functions such as finance, tax, risk, governance, and compliance. The role conducts discovery, prioritizes use cases, defines deployment roadmaps, establishes KPIs, engages executives, aligns initiatives with control frameworks, and guides deployment engineers through implementation and adoption. It requires deep regulated-domain expertise, transformation leadership, executive communication, and cross-functional stakeholder management. Travel may be required for customer engagements and workshops.

Lead the security design, architecture, and implementation for an enterprise AI platform. Define secure deployment patterns, embed security into CI/CD and operations, secure GenAI workflows, and lead customer security reviews and compliance activities for Fortune 1000 deployments.

The role involves designing and deploying scalable GenAI applications using Python and cloud infrastructure, particularly Azure. Responsibilities include model optimization, mentorship of junior developers, and collaborating with teams to align AI solutions with business needs.

Develop and test AI-powered software solutions, troubleshoot challenges, and collaborate with the team to enhance performance while gaining AI experience.