Research Engineer

Entelligence.AI is hiring a Research Engineer to improve its Coding Agent Optimization Engine. The role analyzes agent failures, develops and evaluates prevention strategies, builds an engineering data layer, and optimizes model routing for quality and cost.

Responsibilities:

• Failure mining. Captures coding-agent sessions and links them to the PRs they produced, then mines transcripts and review comments for errors, struggles and recurring root causes
• Learning loop. Turns review findings into root-cause patterns and tested rules, and delivers them to the next agent session as guidance cards through CLI hooks
• Pre-PR judge. A reviewer model checks an agent's change before the PR is opened, and the harness reports back what happened in each session
• Cost-aware routing. Picks a difficulty tier per instruction turn and weights models by expected cost per solved task, with prompt-cache affinity, failover and per-org constraints
• Failure attribution. Given a session transcript and the review comments on its PR, find where the harness went wrong: missing context, a bad tool loop, a skipped check, the wrong model
• Prevention that measurably works. Decide which lessons become rules, cards or judge checks, and prove with held-out data that they cut repeat findings without flooding agents with noise
• Evaluation. Own the benchmark harness of record: replay real agentic tasks headlessly against competing harness configs and routing policies, and measure quality vs. cost honestly
• Engineering world model. Build the comprehensive data layer connecting sessions, PRs, projects, errors and incidents. Leverage the world model to catch issues from happening in future sessions
• Cost and outcome modeling. Turn measured usage, cache shares, latency and review outcomes into continuously refreshed model and harness parameters