Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems.
About the Role
As a seasoned engineer on the Core Infrastructure team at Anthropic, you will be at the forefront of managing and optimizing the compute resources that power our cutting-edge AI systems. You'll work with some of the most advanced hardware available, including TPUs, GPUs, and Tranium chips, across multiple cloud providers to ensure our AI models run efficiently and reliably.
The Core Infrastructure team is responsible for managing all compute resources and infrastructure that power Anthropic's AI systems. We manage hardware across AWS and GCP, ensuring optimal utilization, reliability, and performance at massive scale.
Key Responsibilities
- Lead technical strategy and implementation for critical infrastructure components
- Partner with users across the organization to identify pain points and develop end-to-end solutions
- Collaborate closely with engineering leadership on roadmap planning and strategic direction
- Drive cloud provider customizations and optimizations for our unique hardware and workload requirements
- Develop deep expertise in specific domains while maintaining broad knowledge across all infrastructure areas
- Mentor team members and promote technical excellence across the organization
- Design and implement systems for managing large-scale distributed compute resources
- Lead initiatives to improve reliability, efficiency, and performance of our infrastructure
You may be a good fit if you:
- Deep expertise in Kubernetes management (not just usage but actual administration and customization)
- Proven track record of managing large-scale infrastructure projects
- Strong hands-on coding skills with ability to work across the stack
- Experience with distributed systems architecture and design
- 10+ years of software engineering experience with 4+ years in infrastructure/platform roles
Strong candidates may have:
- Experience with custom silicon or specialized AI hardware (TPUs, GPUs, Tranium)
- Background with large-scale cluster management systems like Tupperware or Borg
- Expertise in networking, particularly in high-performance computing environments
- Experience with scheduling systems and resource optimization
- Knowledge of multi-cloud architectures and cross-provider optimization
Technical Skills
- Expert proficiency in at least one systems programming language (Go, Rust, C++)
- Deep understanding of container orchestration and cluster management
- Experience with Infrastructure as Code (Terraform, CloudFormation, etc.)
- Strong debugging and performance optimization skills
- Knowledge of modern observability practices
You Might Thrive In This Role If You
- Enjoy solving complex technical challenges with real-world impact
- Have a passion for building reliable, scalable infrastructure
- Get excited about working with cutting-edge AI hardware
- Thrive in a fast-paced environment where you can have significant influence
- Are comfortable with ambiguity and taking initiative to drive projects forward
- Love diving deep into technical problems while keeping the big picture in mind
The expected salary range for this position is:
Annual Salary:
$320,000—$485,000 USD
Education requirements: We require at least a Bachelor's degree in a related field or equivalent experience.
Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices.
Visa sponsorship: We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this.
We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed. Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you're interested in this work. We think AI systems like the ones we're building have enormous social and ethical implications. We think this makes representation even more important, and we strive to include a range of diverse perspectives on our team.
How we're different
We believe that the highest-impact AI research will be big science. At Anthropic we work as a single cohesive team on just a few large-scale research efforts. And we value impact — advancing our long-term goals of steerable, trustworthy AI — rather than work on smaller and more specific puzzles. We view AI research as an empirical science, which has as much in common with physics and biology as with traditional efforts in computer science. We're an extremely collaborative group, and we host frequent research discussions to ensure that we are pursuing the highest-impact work at any given time. As such, we greatly value communication skills.
The easiest way to understand our research directions is to read our recent research. This research continues many of the directions our team worked on prior to Anthropic, including: GPT-3, Circuit-Based Interpretability, Multimodal Neurons, Scaling Laws, AI & Compute, Concrete Problems in AI Safety, and Learning from Human Preferences.
Come work with us!
Anthropic is a public benefit corporation headquartered in San Francisco. We offer competitive compensation and benefits, optional equity donation matching, generous vacation and parental leave, flexible working hours, and a lovely office space in which to collaborate with colleagues.
Top Skills
What We Do
Anthropic is an AI safety and research company that’s working to build reliable, interpretable, and steerable AI systems. Our research interests span multiple areas including natural language, human feedback, scaling laws, reinforcement learning, code generation, and interpretability.
