Top Tech Jobs & Startup Jobs in NYC, NY

Design, build, and operate high-scale sandboxing infrastructure for untrusted AI-generated code and agent workloads. Responsibilities include improving startup speed and resource density, strengthening compute, filesystem, and network isolation, debugging kernel and virtualization issues, developing observability tools, supporting reinforcement learning environments, and hardening systems against escape scenarios. The role partners with research, product, platform, security, and cloud teams to deliver reliable, secure sandbox capabilities.

Own full-cycle recruiting across General and Administrative functions, managing high-volume and strategic searches for multiple hiring managers. Use data, systems, AI, and automation to prioritize requisitions, maintain healthy pipelines, identify bottlenecks, and scale repeatable processes. Advise stakeholders on candidate profiles, compensation benchmarks, market conditions, and assessment criteria while protecting candidate experience and cultural alignment.

Own customer evidence and storytelling strategy for Anthropic’s major product launches and company priorities. Identify customers who can validate key messages, develop proof-point libraries, create stories across channels, and support sales enablement. Partner with Product Marketing, Sales, Brand, Creative, Communications, Legal, executives, and customer leaders to secure approvals and shape launch narratives. The role requires strong writing, interviewing, cross-functional influence, technical fluency, and rapid execution.

Design, build, and operate distributed systems supporting reinforcement learning at frontier scale. Responsibilities include scheduling, data movement, storage, networking, fault tolerance, autoscaling, observability, automation, and performance optimization across large accelerator fleets. The role requires debugging complex distributed failures, preserving training correctness, collaborating with researchers, and creating resilient systems that continue operating through hardware failures and changing workloads.

Develops reinforcement learning algorithms, model architectures, and experimental infrastructure for frontier-scale training. Responsibilities include scaling RL across model size, context length, and compute; diagnosing numerical, algorithmic, and systemic failures; optimizing performance and cost; and managing end-to-end large-scale runs. The role requires rigorous experimentation, distributed training expertise, quantitative modeling, and strong programming across the ML systems stack.

Owns the design and operation of a core go-to-market funnel domain in Salesforce, including lifecycle stages, process rules, data models, routing, ownership, forecasting, automation, and integrations. Partners with GTM leadership, RevOps, and developers to translate business needs into system solutions. Builds declarative Salesforce configurations, writes technical specifications, and leads discovery, testing, enablement, and adoption measurement across customer lifecycle processes.

Build and deploy AI systems for life sciences research institutions. Partner with scientific and engineering teams to integrate Claude into research workflows, develop MCP servers, benchmarks, agent skills, and domain-specific infrastructure, and identify deployment challenges involving heterogeneous data and auditability. Create documentation and technical content that enables partners to self-serve and scale successful solutions across the life sciences ecosystem.

Build and operate secure, reliable cloud infrastructure, storage systems, and data pipelines for specialized legal and privacy workloads. Translate legal, privacy, and security requirements into scalable, auditable architectures; manage chain of custody and data integrity; partner with Legal, Privacy, Security, Research, and Infrastructure teams; and deliver production systems under fixed deadlines. The role also requires advising nontechnical stakeholders and extending shared infrastructure for novel legal requests.

Designs and operates large-scale distributed inference systems serving Claude globally. Responsibilities include intelligent routing, load balancing, traffic management, autoscaling, cloud orchestration, deployment pipelines, model serving, accelerator integration, observability, and performance optimization across multiple regions and cloud providers.

Build and ship computer-use and browser-control products across frontend, agent runtime, and backend services. Diagnose agent reliability issues, collaborate with AI researchers, create knowledge-worker workflows, instrument products, analyze usage data, and iterate based on external user feedback. The role requires end-to-end product ownership, full-stack engineering, LLM and agent experience, and comfort operating amid ambiguity.