Judgment Labs — Forward Deploy AI Engineer

Type: Full-time | On-site | San Francisco, CA Compensation: $200,000–$300,000 + 0.1%–0.2% equity Hiring count: 2 Visa sponsorship: H-1B, O-1, OPT (case-by-case for truly exceptional candidates; main scope is candidates who don't require sponsorship) Reports to: Not specified

About Judgment Labs

Judgment Labs builds infrastructure for Agent Behavior Monitoring (ABM). Where traditional observability focuses on logging exceptions and latency, ABM surfaces behavioral anomalies — instruction drift, context retrieval loss, hallucinations — in scaled production environments. Hundreds of teams building autonomous agents rely on Judgment to understand how their systems behave post-deployment, turning real usage data into scoring and feedback loops that continuously improve agent reliability, performance, and decision-making at scale.

Founded: N/A | Team size: under 20 | Total funding: $30M+ across two rounds in the past five months Industry: AI infrastructure / observability / agent tooling Website: judgmentlabs.ai Office: San Francisco

Investors: Lightspeed, SV Angel, Valor Equity Partners, Nova Global, Chris Manning, Michael Ovitz, Michael Abbott, Cory Levy, Kevin Hartz. The team ships at 50+ company velocity — Olympiad medalists, debate champions, and competitive athletes; everyone is either an ex-founder or a founder-to-be.

Why Candidates Should Join

• Founder training ground: The scope, judgment, and autonomy required mirror what it takes to found or lead a technical company — own customer engagements end to end across real production systems.
• Deep technical + customer ownership at a fast, well-funded team: $30M+ raised in five months, top-tier backers, ships at 50+ company velocity with a sub-20-person team.
• Top-of-band comp and perks: $200K–$300K (exceptional AI-savvy FDE leads up to $400K case-by-case), 0.1%–0.2% equity, full benefits, Equinox membership, private chef, direct influence on the product roadmap.

Intake Call Summary

• An intake video is present on the Contrario role page; no text transcript is available, so no intake-call summary could be parsed. Key intent has been drawn from the role body and structured fields below.

The Role

A Forward-Deployed AI Engineer who embeds the ABM platform directly into customers' production systems — integrating monitoring and evaluation into real agent workflows, diagnosing failures in live environments, and driving deployments to reliable production use. Heavily customer-facing: strong communication is as critical as engineering depth. The team is explicitly prioritizing strong software engineers who can flex into a forward-deployed role over candidates with a purely solutions / forward-deployed background.

What You'll Be Doing

• Deploy and embed the ABM platform and AI components directly into customer codebases and production AI systems
• Work inside customer systems to integrate monitoring, evaluation, and agent-facing components into real workflows
• Guide customers through technical decisions around agent monitoring, evaluation strategy, and integration into existing production systems
• Own multiple customer engagements end-to-end, ensuring successful integration and sustained adoption
• Translate customer feedback into roadmap input that shapes the next set of features

Tech stack: Full-stack with backend/infra weight; production AI / LLM-based systems

Requirements

• 3-7 years of software engineering experience, with strong product-engineering ability that ships features end to end inside customer codebases
• Some form of applied AI experience (a hard agent requirement and preferred evals requirement are no longer mandatory; a strong engineer with AI-adjacent exposure is preferred over a pure solutions hire)
• Strong customer-facing communication skills: explains complex technical concepts clearly, builds trust with technical and non-technical stakeholders
• Comfort deploying AI or LLM-based systems into real production environments
• Ability to translate ambiguous customer goals into concrete technical solutions
• The Forward-Deployed Engineer role is their genuine first choice, not a fallback for another role
• Based in SF or willing to relocate; 5 days in person
• Wants to be a technical founder in the future

Green Flags

• Strong software engineering background from a solid product company (e.g. Roblox, Snapchat, Tesla; Databricks-tier a bonus)
• Experience deploying AI or LLM-based systems to production
• Background from infrastructure/observability companies (e.g. Databricks, Datadog, Cognition)
• 4-5+ years of engineering experience (stronger performance and comms); strong juniors still welcome case-by-case
• Genuinely set on the FDE role as their first choice
• Technical background (e.g. coding competitions, research experience) in addition to solutions experience

Red Flags

• FDE is a fallback, not their top choice ("if I can't get X, I'll do FDE")
• Big-tech candidates using FDE as a backup while angling for another role
• Pure FDE/solutions background without real software engineering depth (strong on paper but below the technical bar)
• Failed founder who built the exact product but lacks the prestige/pedigree bar (adds noise and variance)
• Low commitment signals: slow to book or drops off before the interview
• Over-indexed on agent experience at the expense of engineering quality

Role Details

Salary$200,000–$300,000 (Junior/3 yrs: $200K; Mid-senior/3-7 yrs: up to $300K; exceptional AI-savvy FDE leads: up to $400K case-by-case)Equity0.1%–0.2%On-site policyIn-person San Francisco, 5 days in person (Monday–Friday)Visa sponsorshipH-1B, O-1, OPT; case-by-case for exceptional candidates, main scope is no-sponsorshipEmployment typeFull-timeLocationSan Francisco, CA

Screening Questions

1. Where are you currently located?
2. LinkedIn URL
3. Are you legally authorized to work in the United States?
4. Will you require work sponsorship now and/or in the future?
5. Are you located in the San Francisco-area and/or willing to relocate?
6. Are you willing to work on-site in our San Francisco office Monday-Friday?

Interview Process

Stage 1 — Recruiter ScreenStage 2 — Evals FDE RoundStage 3 — Booked Evals Round (scheduling) Stage 4 — Coding IQ RoundStage 5 — Booked IQ Round (scheduling) Stage 6 — FDE OnsiteStage 7 — Offer ExtendedStage 8 — Candidate Hired — Candidate accepts and starts.

Ideal Companies & Backgrounds

Updated June 25, 2026 (all entries shown on page; no "Show all" collapse present)

Palantir Technologies, Databricks, Datadog, Cognition AI, Decagon, Sierra, Linear, Cursor, Ramp, Figma, Vercel, CockroachDB, Modal Labs, Anyscale, Runway, Applied Intuition, Anduril Industries, Mercury, Notion, Nomic AI, MotherDuck

Note: several grid tiles resolved to logo.dev fallback junk and were dropped as artifacts (".Store – Comprehensive Search Directory", "Data Center ISH", "Retoolers", "SearchHounds", "Ponderosa Agency"). "Linear Orbit, Inc." Linear; "andurilindustries.com" Anduril; "Mercury Insurance" likely intended as Mercury (fintech). Confirm if any of these should be reinstated/corrected.

Ideal Candidate Profiles

Labeled on the page as "Ideal Candidates — DO NOT CONTACT." For reference only — do not source these specific profiles. LinkedIn URLs were not captured in the page source.

Yuval Danino · Bhagyashri Badgujar · Smriti Sridhar · Elie Harik · Kabeer Thockchom · Sukrit Rao · Sujan Rachuri · Ishan Mehta · Krrish Chawla · Joseph Tey · Aditya Tadimeti · Sathvik Nallamalli · Aliyan Ishfaq

Rejected Candidate Feedback

• None provided on the role page.