Top Tech Jobs & Startup Jobs in NYC, NY

Build and operate low-latency, highly available distributed data systems and services to deliver high-quality data products that power ML models and product experiences. Partner with data scientists and product teams, design scalable multi-tenant systems, ensure observability and operational reliability, and enable self-service data access.

Lead end-to-end, cross-team programs for Netflix's Hawkins design system; define scope and milestones, manage dependencies and risks, drive cross-platform adoption, align design and engineering partners, and create lightweight processes to deliver scalable UI foundations.

Build and operate scalable, fault-tolerant distributed data infrastructure and platforms. Design and implement RESTful services, tackle multi-threading and reliability challenges, partner cross-functionally to enable data movement, storage, schema management, and developer experience at large scale.

Lead strategy, architecture, and roadmap for Netflix's cloud gaming infrastructure (OS, software stack, telemetry, control plane, transport). Build and grow engineering teams, partner across hardware, encoding, SRE, and product, own hardware fleet economics and operational success, and represent infrastructure tradeoffs to senior leadership while driving low-latency, cost-effective scaling.

Build and operate Tier-0 Pricing, Catalog, and Offers services powering Netflix commerce for 330M members. Design and implement product-facing features and platform infrastructure, solve distributed systems problems (consistency, race conditions, throughput), partner cross-functionally, and own production reliability including on-call incident response.

Lead integration of state-of-the-art AI/ML into Netflixs content platform and publishing tools. Build roadmaps, incubate ML systems from idea to production, partner with researchers and engineers, automate promotional asset creation, optimize distribution and localization, and measure impact with data-driven evaluation.

Design and build scalable training and inference systems for LLMs and multimodal/media models. Optimize end-to-end training (data pipelines, distributed training, mixed precision) and inference (KV cache, batching, quantization). Scale models into production, lead efficiency initiatives, and mentor engineers and scientists to improve latency, scalability, and reliability.

Lead two teams (Consent Data & Privacy and User Data Management) as a manager-of-managers, owning execution and multi-year technical vision for user consent, data collection, storage, pipelines, access, retention, and compliance. Partner with Product, Data Engineering, and Legal to enforce consent and data governance at scale, adapt to evolving regulations, and drive architecture and platform solutions.

Lead product strategy and execution for a portfolio of talent systems (first-party and vendor platforms). Partner with engineering, design, and data science to understand talent data flows, build AI-driven HR products, prioritize roadmaps, and drive launches, adoption, and continuous improvement across the employee lifecycle.

Lead product for Data Developer and Analytics Experiences, defining strategy, roadmaps, and delivery for SQL/Python authoring, semantic layer, developer tooling, visualization, governance, and GenAI integrations to improve productivity and reuse across Netflix analytics.