Top Remote Jobs in Provo, UT

Manage B2B sales accounts, pricing, lead generation, customer relationships, product demonstrations, onboarding, renewals, invoices, and resale partnerships. Develop go-to-market strategies, maintain Salesforce customer profiles, gather product feedback, coordinate agreements with compliance, reconcile payments, and document account processes. The role also supports marketing collateral, exhibitor opportunities, competitor research, and customer usage reporting.

Own full-cycle B2B SaaS sales within a territory, from discovery through negotiation and close. Engage CFOs, Controllers, and finance leaders to identify accounting challenges, deliver value-based demonstrations with Solutions Consultants, forecast revenue, and manage pipeline execution. Collaborate with SDRs and Marketing to expand strategic accounts and drive net-new revenue while consistently meeting or exceeding ARR targets.

Provides hands-on IT support across employee troubleshooting, identity and access management, device lifecycle management, SaaS administration, security compliance, and process improvement. Responsibilities include onboarding and offboarding users, managing permissions and MFA, maintaining laptops and inventories, administering cloud applications, documenting procedures, and resolving technical issues. The role requires working Monday through Friday, 9 a.m. to 5 p.m. Eastern Time, with opportunities for increasing responsibility.

Own a portfolio of client accounts, driving retention, renewals, product adoption, and value realization. Build stakeholder relationships, conduct business reviews, monitor account health, identify risks, support renewals, guide onboarding and training, and relay client feedback. Collaborate with Sales, Support, Product, and Revenue Operations while maintaining Salesforce records and reporting on retention and adoption metrics.

Design, operate, and improve Recharge’s cloud-native infrastructure platform. Responsibilities include Kubernetes and GCP infrastructure management, troubleshooting production issues, maintaining observability and reliability, implementing new technologies, improving engineering standards and documentation, supporting security and automation, and participating in a 24/7 on-call rotation. The role also involves mentoring engineers and applying AI tools to improve team productivity.

Design and scale backend services, data pipelines, event-driven systems, and integrations supporting autonomous manufacturing operations. Build LLM agents and RAG workflows for supplier document analysis, manage PostgreSQL data models, integrate ERP and freight APIs, implement caching and rate limiting, and establish observability and incident response practices. The role requires transactional processing expertise, NLP/ML pipeline experience, and manufacturing supply chain domain knowledge.

Leads platform engineering strategy across Azure infrastructure, legacy Windows Server workloads, infrastructure as code, CI/CD, reliability, identity, security, cost optimization, and engineering tooling. Builds and develops a platform engineering team while overseeing tenant consolidation, modernization, observability, incident response, and developer self-service. Partners with Security and Compliance to support sensitive and regulated data environments.

Own the technical sales process for enterprise security, privacy, AppSec, and GRC buyers. Responsibilities include technical discovery, tailored demos, proofs of value, architecture discussions, security and data-handling objections, vendor reviews, and reusable demo and evaluation materials. Partner with Sales through signature while translating customer feedback into actionable improvements for Engineering.

Leads sales operations, coaches representatives and managers, manages KPIs and CRM processes, oversees training and hiring, supports financing and transactions, and drives accountability, productivity, and revenue growth across sales teams.

Design, build, and productionize LLM-powered systems (adaptive content, tutors, evaluators) and AI pipelines (RAG, embeddings, fine-tuning). Prototype and optimize models across modalities, define AI safety/evaluation/observability practices, and collaborate with product and engineering to measure learner outcomes and iterate.