14 days ago
Base Salary
$280k - $850k/yr
Responsibilities
- Write and maintain software interfacing ML accelerators with high-speed networks.
- Build network systems for accelerator-initiated tensor movement.
- Benchmark software in new networking environments.
- Implement collective algorithms to improve latency.
- Optimize congestion control algorithms for large-scale synchronous workloads.
- Debug kernel-level network latency spikes and complex distributed networking issues.
- Design and maintain higher-level networking abstractions such as collectives and RPC.
Requirements
- Expert-level proficiency with network protocols and networking concepts.
- Deep knowledge of TCP/IP stack internals, XDP, eBPF, io_uring, and epoll.
- Experience with user-space networking technologies including DPDK, RDMA, and kernel-bypass techniques.
- Strong systems programming skills, including memory management, lock-free data structures, and NUMA-aware programming.
- Experience with software, driver, and operating-system performance optimization.
- Comfort with or willingness to learn Rust.
- Bachelor’s degree in a relevant field or an equivalent combination of education, training, and/or experience.
- 5+ years of experience in systems programming or network programming preferred for strong candidates.
- Knowledge of ML accelerators, accelerator drivers, PCIe devices and drivers, SmartNICs, networking algorithms, compression, and graph algorithms is advantageous.
Benefits
- Annual salary range of $280,000–$850,000 USD.
- Hybrid work policy requiring staff to work from an Anthropic office at least 25% of the time, with some roles requiring more office time.
- Visa sponsorship and immigration-lawyer support when applicable.
- Competitive benefits, optional equity donation matching, generous vacation and parental leave, flexible working hours, and office collaboration space.
About Anthropic
We're an AI research company that builds reliable, interpretable, and steerable AI systems. Our first product is Claude, an AI assistant for tasks at any scale. Our research interests span multiple areas including natural language, human feedback, scaling laws, reinforcement learning, code generation, and interpretability.