Career path
AI Engineer
An AI engineer builds products on top of models someone else trained. That means getting the model the right documents and tools, making sure its answers come back in a shape the rest of the app can use, and testing whether a feature actually works. The same title also covers customer-facing deployment jobs and some model-training jobs, so read what a posting says you'd own. Use this path to understand the work, build a project with tests behind it and prepare for the interview.
Read these 10 guides in order
- AI Engineer: A Practical Career and Portfolio Guide
See what the role owns, plus a project that answers questions from documents, with eight test cases.
- AI Engineer vs ML Engineer vs Software Engineer
Tell an AI engineer posting from an ML or software engineer posting before you apply.
- How to Become an AI Engineer: A Path With Proof
Decide what to learn first if you come from software or data, and which portfolio project to build.
- AI APIs for Beginners: Make a Request and Handle the Response
Treat a model call like any call over the network: it can fail, so check what comes back.
- Three Prompt Contracts You Can Check with Evaluation Fixtures
Turn an answer that looks right into written rules, with test examples that pass or fail.
- Build a Small RAG Pipeline and Measure What It Misses
Check who's allowed to see a document before ranking results, check the citations, and measure what the search misses.
- Evaluate a Ticket Agent: Dataset, Scoring Code and Failure Report
Score format, evidence and correctness separately, and keep the failures in the count.
- Run a Bounded Tool-Calling Loop in Python
Keep what the model asks to do separate from what your app actually allows.
- How to Deploy AI Agents to Production
Plan hosting, saved state, spending limits and gradual rollouts for a service that calls a model.
- AI Engineer Interview Guide
Prepare for the coding, system design, evaluation and debugging rounds.
Pay in employer job postings
Checked September 26, 2026. These are yearly ranges from 6 real job postings. They aren't market averages, the jobs aren't all at the same level, and no one is promised these numbers. Open the posting for the current range and the full package.
| Employer and role | Location | Posted range | What the range covers |
|---|---|---|---|
| Baseten AI Engineer | San Francisco (hybrid) | $220K – $260K | The posting's compensation field, plus equity. It does not say whether the range is base salary. |
| Ramp Applied AI Engineer | New York / San Francisco (hybrid) | $204.4K – $352K | The posting's compensation field, plus equity. Final pay depends on location and level. It does not say whether the range is base salary. |
| Brex AI Engineer, Product | San Francisco (hybrid) | $171,000 - $240,000 USD | Expected salary range; starting base pay depends on location, skills and experience. Equity and other compensation may be part of the total package. |
| Gusto Enterprise Application AI Engineer | San Francisco (hybrid) | $225,000-$250,000 /yr | Targeted cash compensation for San Francisco, Seattle and New York; the posting targets $185,000-$205,000 /yr in Denver and most remote locations. Equity (RSUs) is separate. An internal IT role, not a product role. |
| Cognition Applied AI Engineer | San Francisco / London / New York City (on-site) | $180K – $225K | Estimated base salary, plus equity, for a customer-facing role in Customer Engineering. The posting does not give a range by location. |
| Samsara AI Engineer, Customer Success | Remote, United States (outside the SF Bay, NYC and DC metro areas) | $105,910—$160,200 USD | Annual base salary. Performance bonus or variable pay and equity for eligible roles are separate. |
What the job posts name
From 769 postings at 92 employers, read September 2026. The percentage is the share of those employers that name each one.
The full AI Engineer ranking, signature tools and new ones on the radar →
Interview practice
Practice questions written for this site, not leaked questions or any employer’s real interview. Open one to see what a strong answer covers.
Design a help-center assistant for a product with free and enterprise tiers.
Decide who can ask and when the assistant should say no. Filter documents by tier before ranking them, check every citation against what was actually retrieved, and name the first test cases.
A model upgrade improved a public benchmark, but your assistant got worse. What do you do?
Rerun the same saved questions on both versions and look at the answers that changed. Score retrieval, citations and the final answer separately, and explain why the benchmark tests a different task from yours.
Build a test set for a feature that has no labeled examples yet.
Write down how answers get labeled first. Mix everyday cases with rare ones that are expensive to get wrong, keep a set you never tune against, and explain how real incidents become new test cases.
Your test score went from 82 to 88 percent. Is the change better?
Ask how many cases there are and which ones flipped in each direction. Keep failures in the count. A few flips on a small set doesn't prove anything.
The agent sometimes calls the refund tool twice.
Find the missing idempotency key, the tag that lets the system spot a request it already handled. Keep the model's suggestion separate from actually running it, require approval for calls that can't be undone, and add a test that replays the double call.
When would you not use a model for this feature?
Name a rule-based option, compare the cost of mistakes, speed and upkeep, and pick the simpler system when it does the job.
Other AI engineering roles
12-guide path
Forward Deployed Engineer
Work with a customer, find out what is actually stuck, and build the software that makes their workflow run.
10-guide path
Agent Engineer
Build AI software that can take actions, with clear limits and a way to recover when something fails.
5-guide path
GTM Engineer
Fix the data and automate the handoffs a sales or marketing team depends on.
