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Judgmentlabs

Member of Technical Staff, Agents

  • Seen live on Judgmentlabs' site 2 days ago
  • Senior
Pay
Not stated
Location
San Francisco
Open for
12+ days
First seen
Oct 23, 2026
Apply on Judgmentlabs' site (opens in a new tab)

Posting record

No flags

Open 12+ days

The posting's record, oldest first
  1. Judgmentlabs' site dates the posting
  2. Ghostless first sees this listing
  3. Last checked: still up
How we know

It hasn't been reposted, listed twice, or left up unusually long, and the posting doesn't say it's a talent pool.

Ghostless records the day it first sees a posting, so a repost can't reset its age. This one was already up when Ghostless started tracking Judgmentlabs, so its true age is longer.

Last checked Nov 2, 2026.

How well do you match?

See how much of this posting your resume shows, and what it's missing.

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What the posting discloses

Not stated: Pay, real vacancy, AI screening.

Why these three

Ontario requires postings from employers with 25 or more staff to state the pay, whether the vacancy exists, and whether AI screens applicants.

Some employers add pay only on their own site. Check the original posting before you rule it out.

Skills mentioned

About the role

About Judgment

Judgment Labs is building the infrastructure for continual learning in long-horizon AI agents.

We turn long agent trajectories into clean, structured data for evals, labeling, rubric generation, context engineering, and RL workflows. Instead of only showing teams what happened, Judgment helps decide what matters, what should be learned from, and how that learning should flow back into the agent.

We’ve raised $32M across two rounds led by Lightspeed.

The Role

You'll build the product experiences that make this loop legible, and you'll build the agents that run it. This is not a role where you implement specs handed down. You'll own problems end-to-end: talking to customers, defining what to build, building it, and iterating until it's great.

What You’ll Do

  • Judgment Agent: Shape how the Judgment Agent runs large-scale investigations: parallel investigators working across thousands of production traces, each covering a different dimension (failure modes, tool errors, regressions, drift), merging results into one answer.
  • Verification: Build the platform for verifying agent changes: hosted simulated environments for stateful agent evals, trajectory replay against changed agents, and monitors for unintended behavior changes.
  • Agent investigation interfaces: Design how engineers understand what their agents did and why. Long traces, tool calls, decisions, failures. What does debugging look like when the "program" is a reasoning loop? How do you make a thousand-step trajectory legible in minutes?
  • Swarm UX: A hundred parallel investigations is useless if engineers can't follow them. Design how humans watch a swarm work, redirect investigators that go down the wrong path, and consume findings without reading a hundred reports.
  • The improvement loop: Build the workflows that turn production trajectories into datasets, judges, and regression checks, so the path from "found a problem" to "verified a fix" feels like one motion.
  • The platform underneath: Workspaces, roles, permissions, billing, usage, and limits for teams running many agents across many environments.
  • Judgment everywhere agents are built: An SDK and terminal-first experience so Claude Code, Codex, and OpenCode sessions can summon Judgment as a subagent mid-development.

What We’re Looking For

  • Experience building and scaling end-to-end production systems, from data layer to UI.
  • Strong technical problem-solving skills, especially in fast-changing, ambiguous environments.
  • A builder and tinkerer's mindset with high agency - you find creative ways to overcome obstacles and ship.
  • Hands-on experience building with LLMs or agents, or the drive to get there fast.
  • Comfort working directly with customers to understand their needs and solve real-world problems.
  • Excellent communication skills - clear, direct, and persuasive across technical and non-technical audiences.
  • Reinforcement learning, post-training, agents, or machine learning fundamentals.

Why Judgment?

  • You’ll work on problems customers actually feel: Engineers talk directly to teams building production agents, see where their systems fail, and turn those failures into product and infrastructure.
  • Small team, high ownership: You will not own a narrow slice. You’ll shape core systems early, ship quickly, and work across product, data, backend, infra, and customer environments.
  • In person in San Francisco: We work together in person because the problems are hard, the product is moving fast, and the feedback loops matter.

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