Top Tech Jobs & Startup Jobs in Charlotte, NC

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.

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.

Design and execute ML research and end-to-end model development for generative and summary-generation systems. Lead rigorous experiments, translate findings for stakeholders, partner with engineers to deploy models, track state-of-the-art advances, and define roadmaps to improve promotional writing and discovery for members.

Build and operate next-generation ML data and feature infrastructure, including near-real-time feature computation engines, feature stores, and embedding retrieval systems. Scale data processing, storage, and serving for high-throughput training and low-latency inference. Collaborate with ML practitioners and AI Platform teams to improve feature discovery, sharing, and practitioner productivity.

Technical lead for a Context Enablement pod building the context layer for coding agents: plugins, Dev Profiles, and a curated knowledge system (NECP). Define technical strategy, partner with platform teams, ship incremental production work, measure adoption, and mentor a team of engineers to improve developer productivity with agent-enabled tools.