Top Tech Jobs & Startup Jobs in NYC, NY

Own and grow a named North American enterprise account book: qualify opportunities, run full sales cycles, negotiate contracts, navigate security and procurement, and expand ACV through land-and-expand motions. Partner with Solutions Engineering, Customer Success, Legal, and Product to prove value, drive adoption, and build repeatable enterprise playbooks.

Lead technical discovery and design for computer vision solutions using YOLO. Build Python prototypes, train and optimize models, deploy across cloud and edge, run POCs, deliver demos/workshops, and drive customer outcomes while collaborating with Sales, Product, and Engineering.

Manage 30–40 enterprise and high-value mid-market accounts across onboarding, deployment, adoption, expansion, and renewal. Lead technical onboarding, run QBRs and executive reviews, build account and success plans, drive net dollar retention and expansion, collaborate with AEs, SEs, Product and Engineering, and track account health and renewal forecasts in the CRM.

Lead end-to-end creative production for marketing, web, social, events, motion, and print. Implement brand uplift with an agency, lead design QA and research, and collaborate with Growth, Product, and Creative to translate complex vision-AI concepts into clear, on-brand visuals under a fast startup pace.

Lead product marketing for Ultralytics' AI/computer vision products: own positioning, messaging, and GTM launches; create developer-facing content and sales enablement; partner with engineering, product, growth, and community to drive adoption and measurable outcomes.

Lead product strategy, roadmap, pricing, and execution for Ultralytics Platform (annotation, training, deployment). Partner with engineering to ship developer-focused ML SaaS, improve activation/retention, scale a global product team, and engage the open-source community to drive growth and enterprise adoption.

Design and implement novel neural network architectures for YOLO, run large-scale experiments and ablation studies, optimize models for accuracy and efficiency (quantization/pruning/distillation), build training and evaluation pipelines, and collaborate with R&D and engineering to productionize open-source models.