Anthropic · 7 hours ago
Staff Infrastructure Engineer, Pre-training
San Francisco, CA
Full-time
Hybrid
Senior Level, Lead/Staff
$340K/yr - $425K/yr
7+ years expAnthropic is a public benefit corporation dedicated to creating reliable and safe AI systems. They are seeking a Staff Infrastructure Engineer to join their Pre-training team, responsible for developing the infrastructure for large language model training and ensuring the systems are scalable and efficient.
Artificial Intelligence (AI)Foundational AIGenerative AIInformation TechnologyMachine Learning
Responsibilities
Design and implement high-performance data processing infrastructure for large language model training
Develop and maintain core processing primitives (e.g., tokenization, deduplication, chunking) with a focus on scalability
Build robust systems for data quality assurance and validation at scale
Implement comprehensive monitoring systems for data processing infrastructure
Create and optimize distributed computing systems for processing web-scale datasets
Collaborate with research teams to implement novel data processing architectures
Build and maintain documentation for infrastructure components and systems
Design and implement systems for reproducibility and traceability in data preparation
Qualification
Required
7+ YOE outside of internships
Strong software engineering skills with experience in building distributed systems
Expertise in Python and Rust
Hands-on experience with distributed computing frameworks, particularly Apache Spark
Deep understanding of cloud computing platforms and distributed systems architecture
Experience with high-throughput, fault-tolerant system design
Strong background in performance optimization and system scaling
Excellent problem-solving skills and attention to detail
Strong communication skills and ability to work in a collaborative environment
Advanced degree in Computer Science or related field
Experience with language model training infrastructure
Strong background in distributed systems and parallel computing
Expertise in tokenization algorithms and techniques
Experience building high-throughput, fault-tolerant systems
Deep knowledge of monitoring and observability practices
Experience with infrastructure-as-code and configuration management
Background in MLOps or ML infrastructure
Education requirements: We require at least a Bachelor's degree in a related field or equivalent experience
Preferred
Have significant experience building and maintaining large-scale distributed systems
Are passionate about system reliability and performance
Enjoy solving complex technical challenges at scale
Are comfortable working with ambiguous requirements and evolving specifications
Take ownership of problems and drive solutions independently
Are excited about contributing to the development of safe and ethical AI systems
Can balance technical excellence with practical delivery
Are eager to learn about machine learning research and its infrastructure requirements
Benefits
Competitive compensation and benefits
Optional equity donation matching
Generous vacation and parental leave
Flexible working hours
A lovely office space in which to collaborate with colleagues
Company
Anthropic
Anthropic is an AI research company that focuses on the safety and alignment of AI systems with human values.
501-1000 employees
H1B Sponsorship
Anthropic has a track record of offering H1B sponsorships. Please note that this does not
guarantee sponsorship for this specific role. Below presents additional info for your
reference. (Data Powered by US Department of Labor)
Distribution of Different Job Fields Receiving Sponsorship
Represents job field similar to this job
Trends of Total Sponsorships
2025 (105)
2024 (13)
2023 (3)
2022 (4)
2021 (1)
Funding
Current Stage
Late StageTotal Funding
$33.74BKey Investors
Lightspeed Venture PartnersGoogleAmazon
2025-09-02Series F· $13B
2025-05-16Debt Financing· $2.5B
2025-03-03Series E· $3.5B
Leadership Team
Dario Amodei
Co-Founder and CEO
Daniela Amodei
President and co-founder
Recent News
Straits Times
2026-01-23
2026-01-23
Company data provided by crunchbase