Top Tech Jobs & Startup Jobs in NYC, NY

Leads and scales Anthropic’s Enterprise Tech Solutions Architect pre-sales team. Responsibilities include hiring and coaching architects, partnering with sales on adoption strategies, advising strategic customers on AI use cases and deployments, supporting proofs of concept, communicating customer feedback to product and engineering, and representing Anthropic at executive engagements, conferences, webinars, and technical content initiatives.

Analyze product and user data to guide product strategy, operations, and roadmaps. Design experiments, apply causal inference, investigate anomalies, define metrics, build statistical models and simulations, and establish scalable analytics practices. Present actionable insights to technical and business stakeholders while supporting data-informed decision-making for AI products.

Build and deploy LLM-powered tools and agents for life sciences research institutions. Partner with scientific and engineering teams to integrate AI into research workflows, develop MCP servers and reusable agent skills, create scientific benchmarks, address data and auditability challenges, and produce documentation enabling partner self-service and global scalability.

Lead and scale Anthropic’s state and local government sales organization across the United States. Responsibilities include building sales teams, developing go-to-market strategies, managing revenue targets, cultivating relationships with government executives, navigating public-sector procurement, developing channel partnerships, informing product strategy, and ensuring compliant contract execution. The role requires deep state and local government expertise, strong enterprise sales leadership, and experience selling emerging technologies and AI solutions.

Design and launch Anthropic’s global corporate mobile device program across iOS and Android. Own device standards, procurement logistics, enrollment, configuration-as-code, compliance, lifecycle automation, telemetry, managed app distribution, and BYOD boundaries. Build infrastructure using MDM/UEM platforms, cloud services, Terraform, GitOps, and CI. Partner with Corporate Security on device trust and travel devices while driving progressive, telemetry-gated fleet rollouts.

Build and ship full-stack internal products across backend, frontend, and infrastructure. Partner with users to understand workflows, identify high-impact opportunities, create reusable platforms and abstractions, and empower less-technical users. Own product decisions and outcomes, iterate based on feedback, and independently deliver products from concept through launch.

Drive adoption of Anthropic’s AI products among startups by owning the full sales cycle, developing strategic customer relationships, exceeding revenue quotas, identifying new market segments, negotiating complex agreements, and collaborating with product, marketing, and GTM teams. Gather customer feedback to influence product roadmaps, refine sales methodologies, and help startup customers deploy AI safely and effectively.

Design, build, and operate large-scale distributed inference systems serving Claude globally. Responsibilities include intelligent routing, load balancing, traffic management, autoscaling, multi-cloud orchestration, deployment pipelines, accelerator integration, model architecture support, and production performance optimization. The role also enables high-performance infrastructure for AI research and manages multi-region deployments across diverse cloud platforms and accelerator environments.

Develops product risk assessment frameworks and leads safety reviews for new AI features. Evaluates technical and nontechnical harms, misuse, unintended consequences, and harmful outputs; collaborates with product, engineering, legal, policy, and safeguards stakeholders to design mitigations. Creates bespoke evaluations, develops responsible AI policy recommendations, tracks emerging risks, prioritizes safety resources, and reports product policy risks and mitigations to senior leadership.

Build and maintain high-performance distributed inference systems to serve LLMs at scale. Implement intelligent request routing, autoscaling, load balancing, deployment pipelines, and integrate diverse AI accelerators across multi-cloud environments. Use observability to tune production performance and enable research workflows.