Anthropic
AI Frontier · AI lab
- HQ
- San Francisco, US
- Founded
- 2021
- Size
- 1,500+
- Website
- anthropic.com
- Blog
- anthropic.com
Products
Currently tracking 124 active AI roles, with 106 new openings in the last 4 weeks. Primary focus: Agent · Engineering. Salary range $46k–$850k (avg $405k).
AI Frontier · AI lab
Anthropic has 145 active AI-related job listings. The majority of these roles are focused on agents, comprising 28% of the total. Engineering is the most frequent function, with 74 listings, followed by Research with 51. The company is primarily hiring in the United States, with 118 positions, and the United Kingdom, with 22. Frequent tech tags include model_serving, evals, and agent_orchestration, suggesting a focus on deployment and evaluation of AI systems. In the last 30 days, Anthropic posted 16 new AI roles, a 47% decrease compared to the previous 30-day period.
Anthropic currently has 132 active AI-related roles in our index. The most common open titles are: Applied AI Architect, Industries (2), Regional Research Economist, Economic Research (2), Research Engineer, Machine Learning (RL Velocity) (2), Research Engineer, Production Model Post-Training (2), Staff Software Engineer, AI Reliability Engineering (2). Most positions are in Engineering and Research.
Anthropic's active AI hiring is concentrated in: agents (28%), serving infrastructure (17%), post-training (14%). These categories follow a seven-stage AI lifecycle: data, pre-training, post-training, serving infrastructure, agents, evaluation, and application.
Anthropic is hiring AI talent in: United States (106 roles), United Kingdom (20 roles), Canada (6 roles), Ireland (5 roles).
Job postings at Anthropic most frequently reference: model serving, evals, llm observability, agent orchestration, inference infra.
In the past 30 days, Anthropic has posted 29 new AI-related roles. That is a +61% change versus the prior 30 days (18 → 29).
| Title | Stage | AI score |
|---|---|---|
| Staff+ Software Engineer, Inference Runtime Staff+ Software Engineer for Anthropic's Inference Runtime team, focusing on the accelerator-agnostic core of their AI inference serving stack. The role involves setting technical direction, owning the architecture and roadmap, hands-on coding in Rust/Python, optimizing accelerator usage, and building validation systems. Requires deep systems engineering or ML infrastructure background with experience in performance optimization and large-scale distributed systems. | Serve | 9 |
| Security Labs Engineer This role focuses on executing security R&D projects end-to-end, building novel security infrastructure, and driving successful experiments toward production scale. It involves working with research teams to test security controls, evaluating new security technologies, and documenting results to inform future security architecture. The role spans from initial project scoping to potential production deployment, with a focus on high-assurance environments and AI-assisted security tooling. |
| ServeShip |
| 9 |
| Performance Engineer, GPU This role focuses on optimizing GPU performance and systems engineering for large language models, specifically improving utilization and efficiency for inference and training at scale. It involves deep work in GPU programming, custom kernel development, and distributed systems. | ServePretrain | 9 |
| Engineering Manager, GPU (ML Accelerator) Engineering Manager for Anthropic's performance and scaling teams, focusing on optimizing compute resources for inference and training systems. The role involves leadership, technical contribution, bottleneck identification, and ensuring efficiency in large-scale ML systems, with a strong emphasis on GPU/accelerator programming and ML/OS internals. | ServeData | 9 |
| TPU Kernel Engineer TPU Kernel Engineer responsible for identifying and addressing performance issues across ML systems (research, training, inference), with a focus on designing and optimizing kernels for TPUs. Provides feedback to researchers on model performance impact. | ServePost-train | 9 |
| Research Engineer, Discovery Research Engineer focused on building and optimizing infrastructure for AI scientist training, evaluation, and inference. The role involves identifying and resolving infra blockers, developing evaluation frameworks, managing data pipelines, and optimizing training/inference for reinforcement learning in distributed environments. | ServeData | 9 |
| Engineering Manager, Cloud Safety Engineering Manager to lead the Cloud Safety team, responsible for scaling and optimizing Claude's serving infrastructure across Cloud Service Providers (CSPs). The role involves owning end-to-end safety, including API, inference, classifiers, fraud detection, data management, and operations, to ensure safe usage and enable the launch of new models and features at scale. | Serve | 8 |
| Engineering Manager, Inference Engineering Manager for Anthropic's performance and scaling teams, focusing on improving model performance and scaling inference and training systems. Responsibilities include front-line leadership, managing day-to-day execution, prioritizing work, and coaching reports. Requires management experience in technical environments, background in ML/AI, and interest in safe AI development. | ServeData | 8 |
| Performance Engineer This role focuses on optimizing the performance, throughput, and robustness of large-scale distributed machine learning systems. The engineer will identify and solve novel systems problems, implement low-latency sampling, adapt models for low-precision inference, optimize serving efficiency, and design fault-tolerant distributed systems. While not directly building ML models, the role is critical for enabling ML algorithms to run efficiently at scale. | Serve | 8 |
| Staff + Senior Software Engineer, Inference Software Engineer focused on building and maintaining the distributed systems that serve large language models (like Claude) to millions of users. The role involves maximizing compute efficiency, enabling research through high-performance inference infrastructure, and integrating new AI hardware and model architectures. | Serve | 7 |
| Staff + Sr. Software Engineer, Cloud Inference This role focuses on building and optimizing backend services and infrastructure for serving large language models (LLMs) like Claude across multiple cloud service providers (CSPs). The engineer will be responsible for API integration, intelligent request routing, inference execution, capacity management, and day-to-day operations, ensuring reliability, cost-effectiveness, and performance at massive scale. The role involves cross-functional collaboration with internal teams and CSP partners, CI/CD automation, and analyzing observability data. | Serve | 7 |
| Performance Engineer, Inference Systems Performance Engineer for Anthropic's inference fleet (Claude), focusing on throughput, latency, reliability, and correctness. The role involves cross-layer performance investigations, improving correctness evaluation pipelines, building observability tools, and partnering with component teams to implement optimizations. Requires strong performance engineering, Python, and data analysis skills, with a genuine interest in correctness as an engineering discipline. | ServeEval Gate | 7 |
| Staff + Sr. Software Engineer, AI Reliability This role focuses on improving the reliability of AI serving systems, including infrastructure, API layers, and accelerators. Responsibilities include developing SLOs, designing monitoring and observability systems, assisting with high-availability infrastructure, leading incident response for critical AI services, and supporting safeguard model serving. The role requires strong distributed systems and reliability backgrounds, with experience in large-scale model serving infrastructure being a plus. | Serve | 7 |
| Technical Program Manager, Infrastructure Technical Program Manager for Anthropic's Infrastructure organization, focusing on coordinating complex programs across developer productivity, tooling, reliability, and operations for AI systems. The role involves driving strategic initiatives, improving developer workflows, ensuring system reliability, and bridging communication between research, engineering, and product teams. | Serve | 7 |
| Staff + Sr. Software Engineer, Inference Deployment This role focuses on building and maintaining the infrastructure for deploying AI inference code to production across various accelerator fleets (GPU, TPU, Trainium). The core responsibility is to create a continuous, unattended deployment system that optimizes for resource constraints, minimizes cycle time, and ensures reliability at scale. It involves capacity-aware scheduling, deployment observability, and self-service onboarding for new models. | Serve | 7 |
| Technical Program Manager, Inference Performance Technical Program Manager focused on inference performance and efficiency for AI models, coordinating launches, managing dependencies, and optimizing runtime and accelerator performance across multiple hardware targets. | Serve | 7 |