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Ai Agent Developer Jobs in Logan, UT (NOW HIRING)

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Ai Agent Developer information

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$25K

$41.3K

$85.8K

How much do ai agent developer jobs pay per year?

As of Sep 3, 2026, the average yearly pay for ai agent developer in Logan, UT is $41,345.00, according to ZipRecruiter salary data. Most workers in this role earn between $29,300.00 and $44,000.00 per year, depending on experience, location, and employer.

What is an AI agent developer?

An AI Agent Developer designs, builds, and optimizes intelligent software agents that can autonomously perform tasks, make decisions, and interact with users or other systems. This role involves working with machine learning, natural language processing, reinforcement learning, and multi-agent systems to create adaptive and efficient AI solutions. Developers in this field often utilize frameworks like LangChain, AutoGPT, or OpenAI API to enhance agent capabilities. Their work spans various industries, including customer service automation, finance, gaming, and robotics.

What are some common challenges faced by AI agent developers in their daily work?

AI Agent Developers often encounter challenges such as managing large and complex datasets, optimizing agent performance, and ensuring models behave ethically and reliably in unpredictable environments. It’s common to iterate frequently on prototypes, test against edge cases, and fine-tune algorithms based on real-world feedback. Collaboration with data scientists, software engineers, and stakeholders is crucial to understand project goals and adapt solutions accordingly. Overcoming these challenges requires technical flexibility, persistence, and a strong teamwork mindset.

What are the key skills and qualifications needed to thrive in the AI agent developer position, and why are they important?

To thrive as an AI Agent Developer, you need strong programming skills (particularly in Python), a deep understanding of machine learning concepts, and a relevant degree in computer science or a related field. Expertise with AI development frameworks (such as TensorFlow, PyTorch, or OpenAI Gym), cloud platforms, and potentially certifications in AI or data science are common requirements. Creative problem-solving, effective teamwork, and strong communication skills help distinguish top performers in this role. These competencies are essential to designing, implementing, and refining intelligent agents that function reliably in real-world applications.

How to become an AI agent developer?

To become an AI agent developer, you should have a strong foundation in programming languages such as Python, experience with machine learning frameworks like TensorFlow or PyTorch, and knowledge of AI concepts such as natural language processing and reinforcement learning. Gaining relevant education through degrees or online courses and building a portfolio of AI projects can also enhance your qualifications.

Is an AI Agent Developer a good career?

An AI Agent Developer is a promising career path in the technology industry, focusing on creating intelligent systems that can perform tasks autonomously. It typically requires skills in programming, machine learning, and AI frameworks, with strong job growth prospects due to increasing demand for automation and AI solutions. The role offers opportunities for innovation and specialization in emerging technologies.

What job categories do people searching Ai Agent Developer jobs in Logan, UT look for?

The top searched job categories for Ai Agent Developer jobs in Logan, UT are:

What cities near Logan, UT are hiring for Ai Agent Developer jobs?

Cities near Logan, UT with the most Ai Agent Developer job openings:

Ai Agent Developer jobs near you
Infographic showing various Ai Agent Developer job openings in Logan, UT as of August 2026, with employment types broken down into 76% Full Time, 22% Part Time, and 2% Contract. Highlights an 63% Physical, 4% Hybrid, and 33% Remote job distribution, with an average salary of $41,345 per year, or $19.9 per hour.

AI Agent Trajectory Annotator and Reviewer

Bespoke Labs

Logan, UT • On-site

$20 - $30/hr

Full-time

Posted 2 days ago

New

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.

Apply