Top Tech Jobs & Startup Jobs

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 strategy and operations for Anthropic’s Forward Deployed Engineering team, including annual planning, headcount and capacity modeling, business reviews, KPI reporting, commercial and partner economics, organizational design, and cross-functional initiatives. Build models, dashboards, and automated workflows using SQL, Claude Code, and AI agents while partnering with Finance, Legal, Revenue Operations, Sales, Product, Research, and Applied AI.

Own the operating P&L and near-term forecast for Anthropic’s datacenter infrastructure partnerships. Lead site-level financial analysis, budget-versus-actual reviews, cost-reduction initiatives, and operating model decisions. Partner with infrastructure teams, datacenter expansion leaders, and third-party operators to manage lease, power, and operational spending, operationalize budgets, and reduce infrastructure risk.

Advise governments, AI Safety Institutes, and multilateral institutions on frontier AI capabilities, risks, evaluation, and adoption. Develop demos, pilots, prototypes, and evaluation suites; lead technical onboarding, workshops, architecture, and Claude integrations. Collaborate with policy, product, research, and engineering teams to design safe, reliable solutions for public missions, including cybersecurity. Build international partnerships and communicate complex AI concepts to technical and non-technical stakeholders.

Partner Solutions Architect responsible for strengthening Anthropic’s relationships with global and regional system integrators and cloud partners. The role develops joint generative AI solutions, creates reference architectures, supports strategic customer deals, enables partner technical teams, leads technical events, troubleshoots complex issues, and gathers product feedback. Candidates need customer-facing architecture experience, cloud architecture expertise, familiarity with LLM tools or machine learning, strong communication skills, and fluency in Japanese and English.

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.

Lead strategic finance for Anthropic's machines (chips and accelerators). Own capacity economics, evaluate hardware deals, build and run the machines FP&A function, produce hardware forecasts and cash flows, partner with engineering, accounting, treasury, and deal teams to inform hardware ROI and investment decisions.

Lead finance for accelerator compute investments: build detailed bottoms-up financial models, budgeting, forecasting, variance analysis, and capacity planning. Translate technical compute dynamics into financial frameworks, identify optimization opportunities across cloud and chip economics, and partner with engineering and research to inform investment decisions and implement scalable financial planning tools.

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.