We're looking for engineers who love working directly with users and who excel at building data products. The Research Data Platform team builds the tools that Anthropic's researchers use every day to manage, query, and analyze the data that goes into training and evaluating frontier models.
\nAs a Software Engineer on the Research Data Platform team, you will:
\nWe do not require prior ML or AI training experience. If you enjoy working closely with technical users, learning new domains quickly, and building tools people actually want to use, you'll pick up the research context fast.
\nStrong candidates may also have experience with large-scale ETL, columnar storage formats, and query engines (e.g., Spark, BigQuery, DuckDB, Parquet), high-volume time series data , ingestion, storage, and efficient querying, data cataloging, lineage, or metadata management systems, or ML experiment tracking or metrics platforms.
\nXML job scraping automation by YubHub
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\nAs a software engineer on this team, you will:
\nYou may be a good fit if you have significant software engineering experience, particularly building data-intensive applications or internal tooling. You should enjoy working directly with users, gathering requirements iteratively, and shipping things that get adopted. You should also be results-oriented, with a bias towards flexibility and impact.
\nStrong candidates may also have experience with large-scale ETL, columnar storage formats, and query engines, high-volume time series data, data cataloging, lineage, or metadata management systems, ML experiment tracking or metrics platforms, complex data visualization, and full-stack web application development.
\nXML job scraping automation by YubHub
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\nYour responsibilities will include owning the design and evolution of the transactional systems that power the platform, defining architecture for database and storage systems under high-throughput, low-latency workloads, making and driving decisions around data modeling, indexing, replication, and consistency, debugging and resolving complex production issues, establishing standards for reliability, observability, and operability across the platform, partnering with product and research teams to support evolving and often ambiguous requirements, driving improvements in performance, scalability, and cost across the system, mentoring engineers and raising the bar for system design and technical decision-making.
\nKey qualifications include 10+ years of experience building and operating production systems at scale, with ownership of critical infrastructure, strong experience designing and operating transactional systems and databases, deep understanding of data modeling, indexing, transactions, concurrency, and consistency tradeoffs, experience owning systems with strict reliability and performance requirements in production, strong experience debugging complex production issues and reasoning about failure modes, experience designing distributed systems or large-scale infrastructure where tradeoffs are non-trivial, proven ability to define architecture and drive technical decisions end-to-end, strong judgment in balancing performance, reliability, and cost, ability to operate effectively in ambiguous, fast-moving environments with high ownership.
\nPreferred qualifications include experience with database internals, storage systems, or query engines, experience building infrastructure for AI/ML systems or data platforms, experience in early-stage or high-growth environments.
\nXML job scraping automation by YubHub
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