Staff+ Software Engineer, Storage + Transfer
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Job description
Storage plays an essential role at Anthropic across research, development, product, and running the business. Our exabyte-scale, multi-cloud storage infrastructure underpins training, CI, sandboxing, inference, warehousing, and long-term data retention. Our storage needs are growing rapidly with Claude. To keep up, we’re looking for engineers who have designed, secured, operated, scaled, governed, and optimized blob storage platforms at hyper-scale.
You’ll join a welcoming, collaborative, high-trust, high-velocity team that is devoted to our users and Anthropic’s mission. You’ll bring deep experience, essential technical guidance, user-centric product sense, and hands-on significant contributions to help us evolve Anthropic’s storage capabilities while absorbing increasing volume and complexity. You’ll own high-stakes architectural decisions and deliverables to make stored data securely and speedily accessible across varying infrastructures and regions. You’ll collaborate closely with other orgs to drive world-class security, usability, reliability, and capacity/cost efficiency: both for the data we store, and the ways we make it accessible to power a wide range of workflows.
You’ll partner with research, training, infrastructure, business, and data teams to understand our storage needs and opportunities, today and in the future. You’ll drive strategic investments that uplevel Anthropic’s ability to ship safe, useful AI at speed., * Shape the technical strategy and architecture for Anthropic’s storage layers
- Build strong relationships with our users and partner teams, and deep understanding of their access patterns and unique security, usability, and business requirements: everything from large-scale ML and training workloads, to financial data processing with strong controls
- Partner closely with CSPs, networking, and datacenter teams on infrastructure primitives
- Translate user needs into scalable, achievable system designs and drive alignment
- Make principled tradeoffs across durability, availability, consistency, performance, security, and cost, and document the reasoning so other engineers and teams can build on it
- Break large problems into deliverable milestones, and lead cross-functional teams to ship new capabilities safely at unprecedented speed
- Work across backend stacks, abstraction layers, and clients to provide users with simple, consistent interfaces no matter where data lives
- Plan and lead large migrations of critical workloads with user minimal disruption
- Participate in and improve operations, including SLOs, observability, capacity planning, incident response, and on-call
- Stay hands-on in code and production, including in the most critical and complex areas
Requirements
- Experience designing, building, and operating a large-scale distributed storage system in production, such as an object store, distributed file system, or block storage service.
- Deep understanding of storage system fundamentals, including: Replication and erasure coding, consistency models, metadata management, durability, failure handling, high availability, access controls, identity models, security approaches, abstraction layers, network requirements, multi-region considerations
- Experience serving as an owner, tech lead, or architect for a large, complex infrastructure system that orgs and customers depend on
- Strong software engineering fundamentals and hands-on coding ability in at least one systems language, such as C++, Rust, Go, or Java
- Experience operating and steering critical infrastructure at scale, including security, on-call, incident response, capacity planning, customer support, and cost efficiency
- Strong written and verbal communication skills, with experience driving alignment on technical direction and user-facing across multiple orgs and stakeholders, Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience
Required field of study: A field relevant to the role as demonstrated through coursework, training, or professional experience
Minimum years of experience: Years of experience required will correlate with the internal job level requirements for the position
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.
Benefits & conditions
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. Guidance on Candidates’ AI Usage: Learn about our policy for using AI in our application process.
About the company
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., 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.
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