AI Engineer

Momentic, San Francisco

What you will do

Fine-tune models for long-running agents, including agents that investigate whether test failures come from infrastructure, recent code changes, or test authorship.
Build and curate training and evaluation datasets from production workflows, using failure patterns to identify opportunities for improvement.
Develop LLM evaluations and observability that connect offline results with production behavior and help the team verify improvements.
Tune prompts, context, and model behavior to improve task success, reliability, latency, token usage, and cost.
Design repeatable experiments and help publish benchmarks that demonstrate our core agents’ performance on testing and browser workflows.
Own technical projects from problem definition through delivery, working with the engineering team to make practical tradeoffs and ship improvements.

Momentic, San Francisco

$175K–$250K

a year

This base pay is near the median for ML and AI engineering jobs in the Bay Area with no level in the job title.

We compare the middle of each posted range. This is base pay as posted. The group does not include this job.

The Momentic site opens in a new tab.

Money raised: At least $18.6MSum of SEC filings. A filing does not show all money that a company raises.
Newest raise: Nov 14, 2025First sale on an SEC filing

Funding records can have errors and can be missing. The company page has the links.

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Fine-tune models for long-running agents, including agents that investigate whether test failures come from infrastructure, recent code changes, or test authorship.
Build and curate training and evaluation datasets from production workflows, using failure patterns to identify opportunities for improvement.
Develop LLM evaluations and observability that connect offline results with production behavior and help the team verify improvements.
Tune prompts, context, and model behavior to improve task success, reliability, latency, token usage, and cost.
Design repeatable experiments and help publish benchmarks that demonstrate our core agents’ performance on testing and browser workflows.
Own technical projects from problem definition through delivery, working with the engineering team to make practical tradeoffs and ship improvements.