Anthropic currently has 151 active AI-related job listings, with a significant focus on roles related to agents, which constitute 32% of their openings. Engineering is the most frequent function, followed by Research. The majority of their hiring is concentrated in the United States. Frequent technical tags include evals, model_serving, and agent_orchestration, suggesting a focus on the practical deployment and management of AI systems.
Currently tracking 127 active AI roles, down 32% versus the prior 4 weeks. Primary focus: Agent · Engineering. Salary range $46k–$850k (avg $406k).
Anthropic currently has 149 active AI-related roles in our index. The most common open titles are: 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), Product Manager, Safeguards Rare Harms. Most positions are in Engineering and Research.
Anthropic's active AI hiring is concentrated in: agents (31%), serving infrastructure (15%), post-training (15%). 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 (130 roles), United Kingdom (18 roles), Canada (6 roles), Ireland (3 roles).
Job postings at Anthropic most frequently mention: Machine Learning, AI Safety, Production ML Systems, System Design, Large Language Models (LLMs).
In the past 30 days, Anthropic has posted 32 new AI-related roles.
| Title | Stage | AI score |
|---|---|---|
| Machine Learning Infrastructure Engineer, Safeguards Research Machine Learning Infrastructure Engineer for Safeguards Research at Anthropic. Focuses on building and scaling infrastructure, data pipelines, and tooling for ML research, specifically for detection and mitigation of AI misuse. Owns training, evaluation, and scoring workflows, aiming to improve iteration speed, throughput, cost, and reliability of inference and scoring workloads. Bridges research and production by creating reliable, production-grade jobs from research workflows. | ServePost-train | 9 |
| 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 |
| 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, 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 |
| Engineering Manager, Search Engineering Manager to lead the Search Platform team, responsible for the search stack behind Claude, including indexes, retrieval, ranking, and serving infrastructure. The role involves owning the strategy, roadmap, search quality, and operating the platform at scale, with a product dimension to define search experience. Requires strong technical depth, product mindset, and experience managing engineering teams and search systems. | ServeAgent | 7 |
| Staff + Senior Software Engineer, Inference Deployment Software Engineer focused on designing and building the deployment infrastructure for AI inference services across various hardware accelerators (GPUs, TPUs, Trainium). The role involves optimizing deployment orchestration, capacity-aware scheduling, and observability to ensure safe, quick, and uninterrupted production releases, managing resource constraints and minimizing cycle time from code merge to production. | Serve | 7 |
| 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 Launch Engineering Staff + Sr. Software Engineer role focused on scaling and optimizing Claude's inference on cloud platforms (AWS, GCP, Azure). Responsibilities include owning the end-to-end product of Claude on each cloud platform, API integration, intelligent request routing, inference execution, capacity management, and day-to-day operations. The role involves validating inference server and load balancer changes, ensuring correctness, performance, and reliability. Key tasks include bringing up inference for new model architectures, integrating new inference features, fixing cross-platform differences, designing and owning CI/CD infrastructure, driving down cycle time for validation, and analyzing observability data for bottlenecks and regressions. | 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 |
| 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 |
| Staff+ Software Engineer, Platform Staff+ Software Engineer, Platform at Anthropic. This role focuses on building foundational primitives, infrastructure, and systems that accelerate product development and enable reliable, scaled shipping of AI products. Responsibilities include architecting and optimizing development infrastructure, service mesh, observability, CI/CD, multi-cloud operations, identity and authentication, tool call proxying, and API distributability. The role also involves building training systems for customer workload adaptation and working closely with research and agent platform teams. Requires 8+ years of experience in backend/platform engineering, distributed systems, cloud-native products, and proficiency in languages like Python, Go, or Rust. | Serve | 5 |