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Understand what is changing in AI evaluation—and what you can learn from it. Research, benchmarks, tools, practitioner lessons and opportunities.
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Last editorial review: 9 September 2026. This is a curated selection, not a live feed. Source dates are shown below. Company job links do not establish that a vacancy was created by the announcement.
Career explainer · 9 September 2026
Some opportunities rely on domain judgment, clear instructions and operational skills. Our new guide uses official employer examples to explain the requirements and distinguish employment from vendor projects.
Hiring selection · 9 September 2026
Our first hiring edition explains the teams, work, locations and advertised pay behind five selected vacancies.
Company announcement · 3 September 2026
The companies announced a platform serving open-source drug-discovery models on infrastructure in Japan.
Why it matters for this audience: specialist applications raise questions about which tasks and domain-specific outcomes a model should be tested on. That is our editorial interpretation; the announcement does not establish new evaluation vacancies.
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The company's post-training vacancy is a general role; we have not verified a connection to JapanFold.
Foundational reading · 9 January 2026
Anthropic's engineering guide explains agent evaluations, grading and the practical challenges of testing systems that take multiple steps.
Read the engineering guide · Start with our plain-English introduction · Explore evaluation roles
A starting library for understanding the work. These are ongoing reference resources, not announcements of new releases. Documentation can change.
How tasks, trials, graders and environments fit together—and why a score needs context.
Anthropic: evaluating agentsUnderstand evaluation datasets and the distinction between development testing and production evaluation.
LangSmith: evaluation conceptsExplore the components of a task, including instructions, an environment and verification.
Harbor: task documentationUse recorded actions to investigate where a system went wrong.
LangSmith: observabilityWe cover useful developments even when no company is hiring. Each editorial item should link to its original source, show its date, explain the practical significance and distinguish reported claims from our interpretation. Benchmark results need their task and testing conditions; a higher score alone does not prove a better system for every use.
Tool inclusion is for learning, not an endorsement. Company funding is not revenue or proof of job security. Vacancies are linked to specific announcements only when that connection is verified. Corrections and material updates receive an editorial date.
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