Anthropic

3 months ago

Research Engineer, Knowledge Foundations

✓ Verified Job

Canonical indexed version, validated from employer's careers page.

Source: Company careers pageValidated by: Fursa AI

Last checked: 23 hours agoStatus: Live

Job Summary

Seeking a Research Engineer to own reliability, observability, and infrastructure for Knowledge Work training environments. You’ll harden systems, run realistic load tests, surface issues with lean instrumentation, and serve as the primary contact for partner teams to ensure stable, trustworthy evaluation results that enable researchers to focus on research.

Location: San Francisco

Workplace: Onsite

Employment Type: Full time

Job Function: Research & Scientific (R&D)

Key Responsibilities

•Serve as the dedicated reliability owner for the Knowledge Work training environments, providing continuity of context and reducing the operational overhead of rotating ownership
•Own a clean, canonical set of evaluation tools and processes for Knowledge Work capabilities, including the process used for model releases
•Build and automate observability, dashboards, and operational tooling for our training environments and evaluation systems, with an emphasis on high signal-to-noise: a small set of trusted metrics and alerts rather than sprawling instrumentation
•Proactively harden environments and evaluation systems through load testing, fault injection, and stress testing at realistic scale, so failures surface early rather than during critical training work
•Act as the primary point of contact for partner training and infrastructure teams when issues in our environments arise, and drive incidents to resolution
•Reduce the operational burden on researchers so they can stay focused on research

Pay and Benefits

Salary: USD 350,000 - 850,000 annually

Key Requirements

•Highly experienced Python engineer who ships reliable, well-instrumented code that teammates trust in production
•Demonstrated experience operating ML or distributed systems at scale, including significant on-call and incident-response experience
•Strong SRE or production-engineering mindset — reaching for SLOs, load tests, and failure injection before reaching for more dashboards
•Foundational ML knowledge sufficient to understand what a training environment or evaluation is actually measuring, and recognize when an evaluation has become stale or gameable
•Able to read research code and reason evaluation integrity

Experience:AIMLDistributed systems

Education:Bachelor's

Skills:PythonObservabilitySRELoad testingFault injectionInstrumentation

Languages:English

Tech Stack:PythonObservabilitySRELoad testingFault injectionInstrumentation

Eligibility

Work Authorization:Sponsorship available.

Company Brief

Anthropic

Develops large-scale AI systems and safety research to create reliable, steerable, and interpretable AI assistants and models for commercial and research applications.

Industry: AI & Machine Learning

Company Size: Enterprise (1,001+ employees)

Growth: Scaleup

Valuation: Unicorn (USD 1B+)

Funding: Series C

Headquarters: San Francisco, United States

Founded: 2021

Website LinkedIn

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Anthropic

3 months ago

Research Engineer, Knowledge Foundations

✓ Verified Job

Canonical indexed version, validated from employer's careers page.

Source: Company careers pageValidated by: Fursa AI

Last checked: 23 hours agoStatus: Live

Job Summary

Location: San Francisco

Workplace: Onsite

Employment Type: Full time

Job Function: Research & Scientific (R&D)

Key Responsibilities

•Serve as the dedicated reliability owner for the Knowledge Work training environments, providing continuity of context and reducing the operational overhead of rotating ownership
•Own a clean, canonical set of evaluation tools and processes for Knowledge Work capabilities, including the process used for model releases
•Build and automate observability, dashboards, and operational tooling for our training environments and evaluation systems, with an emphasis on high signal-to-noise: a small set of trusted metrics and alerts rather than sprawling instrumentation
•Proactively harden environments and evaluation systems through load testing, fault injection, and stress testing at realistic scale, so failures surface early rather than during critical training work
•Act as the primary point of contact for partner training and infrastructure teams when issues in our environments arise, and drive incidents to resolution
•Reduce the operational burden on researchers so they can stay focused on research

Pay and Benefits

Salary: USD 350,000 - 850,000 annually

Key Requirements

•Highly experienced Python engineer who ships reliable, well-instrumented code that teammates trust in production
•Demonstrated experience operating ML or distributed systems at scale, including significant on-call and incident-response experience
•Strong SRE or production-engineering mindset — reaching for SLOs, load tests, and failure injection before reaching for more dashboards
•Foundational ML knowledge sufficient to understand what a training environment or evaluation is actually measuring, and recognize when an evaluation has become stale or gameable
•Able to read research code and reason evaluation integrity

Experience:AIMLDistributed systems

Education:Bachelor's

Skills:PythonObservabilitySRELoad testingFault injectionInstrumentation

Languages:English

Tech Stack:PythonObservabilitySRELoad testingFault injectionInstrumentation

Eligibility

Work Authorization:Sponsorship available.

Company Brief

Anthropic

Develops large-scale AI systems and safety research to create reliable, steerable, and interpretable AI assistants and models for commercial and research applications.

Industry: AI & Machine Learning

Company Size: Enterprise (1,001+ employees)

Growth: Scaleup

Valuation: Unicorn (USD 1B+)

Funding: Series C

Headquarters: San Francisco, United States

Founded: 2021

Website LinkedIn