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Manager Ai Agent Engineer Jobs in Arizona (NOW HIRING)

AI Engineer

Phoenix, AZ

$50K - $112K/yr

... agent harnesses and implementing context engineering, memory management, retry logic, and structured output validation to support reliable, multi-step AI workflows - Demonstrating proficiency in ...

AI Quality Engineer Lead

Phoenix, AZ · On-site

$71K - $92K/yr

AI Quality Engineer Lead Role: Onsite, Phoenix, AZ This role will own the end-to-end quality ... and agent lifecycle management. AI/LLM Validation & Assurance Ability to define and implement ...

Product Manager, AI

Tempe, AZ · On-site

$150 - $200/hr

Title Product Manager, AI Location Hybrid-New York, Tempe, or San Francisco About Us Wealth.com is ... Partner closely with engineering teams to deliver with quick time-to-market results and optimized ...

Strong knowledge of prompt engineering , AI orchestration, and model evaluation. * Experience with ... Knowledge of MCP (Model Context Protocol) or AI agent architectures. * Experience with Python for ...

Manager IT/ AI

Marana, AZ · On-site

$105K - $130K/yr

Join Our Team as a Manager, IT & AI at Trico Electric Cooperative! Posting Period: Thursday July ... Direct AI agent development and orchestration, including use‑case design, prompt engineering ...

Showing results 21-40

Manager Ai Agent Engineer information

What is the difference between Manager Ai Agent Engineer vs Data Scientist?

AspectManager Ai Agent EngineerData Scientist
Required CredentialsBachelor's or Master's in CS, AI, or related fields; experience with AI/ML toolsBachelor's or Master's in CS, Statistics, or related fields; proficiency in data analysis and modeling
Work EnvironmentDeveloping, deploying, and managing AI agents; cross-functional teamsAnalyzing data, building models, and deriving insights; research-focused
Employer & Industry UsageTech companies, AI startups, enterprises implementing AI solutionsTech firms, finance, healthcare, research institutions

The Manager Ai Agent Engineer focuses on leading AI agent development and deployment, managing teams, and ensuring AI solutions meet business needs. In contrast, Data Scientists primarily analyze data, build predictive models, and generate insights. Both roles require strong technical skills, but their core responsibilities and work environments differ significantly.

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AI Agent Trajectory Annotator and Reviewer

Bespoke Labs

Phoenix, AZ • On-site

$20 - $30/hr

Full-time

Posted 7 days ago


Job description

Type: Contract, hourly

Location: Remote

Hours: 20–30 per week

Pay: $20–30/hour, based on experience and language coverage

Start: Immediate

ABOUT THE ROLE

We evaluate how well advanced AI coding agents solve real engineering problems. An agent is given a real open source codebase inside a container and a hard task, then works on its own for 80 to 250 steps. A trajectory is the full record of that run — every command, result, and decision.

You will do two jobs, and you should expect either on any given day.

•Annotate — Read a trajectory nobody has looked at yet and judge it step by step.

• Review — Take an existing annotation, written by our AI tooling or another person, and confirm, correct, or reject it.

TASKS YOU'LL SEE

• Feature build — Add a working feature to a live library without breaking anything that already worked.

• Rebuild — Work out what a compiled tool does by running it, then rebuild it to match its output, exit codes, and file effects.

• Bug hunt — Find and fix twenty undocumented bugs across a dozen files with no test suite, then record what caused them.

Mostly Python and Go, with some Rust, C, and Ct+. A trajectory runs about 100 steps.

WHAT YOU JUDGE IN A TRAJECTORY

• Was the command right for the state the environment was actually in?

• Did the agent read the previous output correctly?

• Was the step wrong, or only inefficient — these are scored differently.

• Where did the run first go off course — usually earlier than where it visibly broke.

• Did the agent notice its own mistake and recover, or keep building on a false assumption?

• Did it game the grader instead of solving the task (e.g., weakening a test or hardcoding an expected value)?

WHAT WE NEED FROM YOU

• Experience — 2+ years in software engineering, DevOps, or site reliability, with real debugging in real codebases.

• Languages — Strong in Python or Go, and able to read a language you've never used.

• Linux — Comfortable with logs, running processes, build failures, and containers.

• Workflow — Everyday Git, diffs, pull requests, and issue tracking.

• Debugging — Able to work with no test suite and no error message pointing at the cause.

• Focus — Able to hold context across a long run, because step 74 can depend on step 12.

• Writing — Clear English, since every judgement needs an explanation another engineer can check.