Top Tech Jobs & Startup Jobs

Own sales operations and deal desk processes, including pricing approvals, forecasting, pipeline and performance analysis, territory and quota planning, KPI governance, data quality, and variable compensation reporting. Partner with sales leadership, finance, and systems teams to structure enterprise deals, maintain pricing guardrails, explain revenue metrics, and improve quote-to-order workflows.

Build and scale Salesforce and broader go-to-market systems, including data models, forecasting workflows, integrations, data quality, marketplace revenue infrastructure, compensation support, and AI-powered workflows. Partner with Revenue Operations, sales, finance, marketing, data engineering, and leadership to deliver tested, source-controlled solutions and support end users across lead routing, assignments, campaigns, and GTM processes.

Deploy production computer vision systems for new customers, including data pipelines, model optimization, edge hardware configuration, infrastructure integration, troubleshooting, and knowledge transfer. Work closely with customer engineering and operations teams in physical environments, identify deployment risks, document architectures, and provide product feedback. The role requires strong Python skills, production ML deployment experience, edge computing knowledge, and travel of approximately 40–50%.

Own corporate IT operations across identity, access, macOS devices, SaaS applications, hardware, onboarding, offboarding, and employee support. Administer SSO and MDM platforms, manage SaaS licenses and vendors, automate repetitive workflows, maintain documentation and asset inventories, support office IT, and assist with security controls, access reviews, compliance audits, and evidence collection.

Build, secure, scale, and maintain cloud infrastructure supporting SaaS products, databases, storage, search, microservices, and machine-learning pipelines. Operate Kubernetes workloads, automate infrastructure with Terraform and Helm, improve reliability and observability, optimize costs, define SLOs and SLAs, participate in incident response and on-call rotations, address vulnerabilities, support customer security integrations, and maintain compliance with SOC 2, HIPAA, and GDPR requirements.

Help self-serve and corporate customers become successful, independent Roboflow users through technical onboarding, webinars, workshops, office hours, targeted enablement, and reusable educational content. Teach computer vision workflows including datasets, annotations, model training, evaluation, and deployment. Identify recurring customer friction, create scalable learning programs and documentation, monitor engagement, measure program effectiveness, and collaborate with Product, Support, Sales, and Field Engineering.

Lead and scale Roboflow’s SMB and Mid-Market sales organization. Responsibilities include managing and coaching Account Executives, owning revenue performance, improving pipeline generation and conversion, building repeatable sales processes, establishing forecast and operating cadences, hiring and developing sellers, and collaborating cross-functionally to refine segmentation, positioning, and go-to-market strategy.

Leads and scales Roboflow’s Sales Development organization, owning SDR team performance, pipeline generation, outbound strategy, account targeting, enablement, analytics, hiring, onboarding, and career development. Partners with Sales, Marketing, Revenue Operations, and Recruiting to build a scalable SDR operating system, improve opportunity quality and conversion, support technical buyer engagement, and establish a strong SDR-to-AE talent pipeline.

Build and manage Roboflow Labs’ frontier-lab business development pipeline. Own the full sales cycle, including outreach, discovery, evaluations, pricing, proposals, negotiation, procurement, legal coordination, and closing. Establish repeatable CRM, forecasting, pipeline-review, and account-planning systems while developing durable customer relationships. Represent Roboflow Labs at industry events and engage research scientists, human-data teams, technical executives, procurement, and legal stakeholders.

Conduct applied AI research focused on computer vision, multimodal, robotics, and coding models. Design datasets, evaluations, benchmarks, and reinforcement learning environments; analyze model failures; develop data remediation specifications; validate data quality through fine-tuning and ablation studies; contribute to open models; publish research; and collaborate with frontier labs to translate model roadmaps into data programs.