AI Evaluation & Reliability Engineer
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- Build valid evaluation sets, graders and adversarial test foundations
- Automate continuous evals and operate observable AI systems
- Evaluate agent trajectories and govern production release quality
Benchmark: Anthropic Model Evaluations + OpenAI Frontier Evals & Environments / Codex Research (current Sep 2026) Market refresh: evaluation science + benchmark quality, graders/golden sets, adversarial and agent evals, continuous regression/variance analysis, traces/observability, SLOs, release gates and quality-latency-cost reliability tradeoffs.
Who’s Hiring Right Now
US openings posted in the last 7 days at OpenAI — this role’s own benchmark employers — refreshed daily, as of 2026-11-01. Pulled directly from each company’s public job board; not exhaustive, and postings close fast — worth confirming the details on the employer’s own site before applying.
Enrichment
Build valid evaluation sets, graders and adversarial test foundations
Evaluation design, graders + benchmark quality
Coverage: AI Evals & Monitoring / Model Evaluation / Statistics
Robustness, adversarial + safety evaluation
Coverage: AI Red Teaming & Adversarial Testing / AI Safety
Automate continuous evals and operate observable AI systems
Golden sets, regression gates + continuous eval pipelines
Coverage: AI Evals & Monitoring / LLMOps / CI-CD
Observability, traces + SLO / incident reliability
Coverage: Observability & SRE / Incident Response / AI Monitoring
Evaluate agent trajectories and govern production release quality
Agent trajectories, tool-use + long-horizon evals
Coverage: AI Agents / AI Evals & Monitoring / Agentic Systems
Release gates, SLOs + quality/latency/cost tradeoffs
Coverage: Launch Readiness / Reliability / AI Product Quality
Role-Readiness Proof / Capstone
Build an eval-and-reliability system for a tool-using agent: define task taxonomy and release criteria; create versioned golden sets with human labels and hard/adversarial slices; implement deterministic checks, model graders and trajectory/tool-use evals; measure agreement, variance and regression; add traces plus quality, latency, cost and error SLOs; red-team tool boundaries; simulate a failure; then issue a release/rollback decision with an eval report and runbook.
Proof Rubric
- Prove measurement validity and production judgment: representative test data
- reproducible runs
- grader calibration and human agreement
- leakage/contamination controls
- adversarial coverage
- regression sensitivity and uncertainty
- traceable failure taxonomy
- agent/tool correctness
- observable SLOs
- quality-latency-cost tradeoffs
- safe release/rollback
- show how eval evidence changed the system rather than merely improving a benchmark score