Top Tech Jobs & Startup Jobs in NYC, NY

Lead and scale a Forward Deployed Engineering team embedding with enterprise customers to build production-grade AI-native workflows. Own delivery quality, stakeholder relationships, scoping, executive technical sponsorship, deal strategy, hiring, coaching, and partner enablement to ensure fast, repeatable, high-impact engagements.

Lead ambiguous product projects for developer-focused AI coding tools: prototype, ship, iterate, run customer discovery, partner with engineering/design/sales, own launches and metrics, and build tooling that scales for individual and enterprise users.

Lead and scale community program operations and event logistics (Ambassadors, Campus), build and streamline infrastructure and workflows, identify and remove inefficiencies, support global program growth, and engage directly with developer community members.

Build and improve large-scale GPU compute, storage, and software infrastructure to support training of agentic coding models. Work with ML researchers, cloud/OEM partners to design GPU clusters, improve training throughput, reliability, scheduling, data movement, automation, and developer experience across cloud and bare-metal environments.

Design, build, and operate ML-focused data infrastructure and pipelines that capture telemetry and model signals. Own, refactor, or replace systems for correctness, privacy, consistency, cost, and maintainability. Instrument new product surfaces, fix gaps, implement schema evolution and validation, and optimize storage/retention to support model and product teams.

Lead and grow a team of AI Deployment Managers to scale enterprise post-sale adoption. Own account planning, health, risk mitigation, expansion, and complex escalations. Build scalable deployment processes, guide rollout and prompt design best practices, and partner with Sales, Engineering, and Product to drive customer outcomes and product adoption.

Lead a team building high-signal evaluation datasets, developer tools, and online evaluation systems (e.g., CursorBench) to measure and improve coding agent/model quality, integrate evals into launches, deploys, and training decisions, and define metrics and guardrails.

Build platform-level primitives to enable resilient, easy-to-operate services: service provisioning, deploy tooling, workflow orchestration, and service ownership contracts. Define abstractions, developer-facing standards, and tooling that improve reliability and developer productivity across teams.

Build distributed training, inference, and RL infrastructure; create libraries for large-scale data jobs; architect systems converting user data into training data; collaborate with researchers to accelerate iteration and reproducibility.

Lead and build a technical curriculum function: create roadmaps, author code-focused learning content, partner with Product and Engineering for launches, define metrics for developer activation and fluency, and hire and grow a team of education engineers.