Evaluation Methodology: Define metrics and protocols that assess safety alongside model usefulness, using baselines, uncertainty estimates, and failure analysis to distinguish meaningful improvements from noise.
Reproducible Evaluation: Build reusable Python pipelines with versioned datasets, model configurations, and experiment tracking to reproduce results and compare performance across model releases.
Automated Red-Teaming: Develop tools to discover failure modes and expand evaluation coverage, incorporating human review to validate automated scoring and provide additional judgment.
Failure Analysis: Investigate model failures with researchers, document actionable findings, and develop regression tests to assess whether model changes resolve identified issues.
Results and Communication: Partner with platform engineers to store and aggregate evaluation results, and clearly communicate findings, limitations, and emerging risks to researchers and decision-makers.
What We Need To See
Education and Experience: Bachelor’s degree or equivalent experience, with 6+ years in the field of machine learning engineering or evaluation.
Evaluation Design: Experience developing benchmarks, metrics, and statistically sound comparisons for ML systems, including selecting baselines and interpreting uncertainty. Ability to translate ambiguous evaluation questions into testable experiments.
Engineering: Strong Python skills and experience building maintainable shared tools, data pipelines, and experiment-tracking workflows.
Analytical Rigor: Ability to assess dataset quality, identify limitations in metrics and scoring methods, and support conclusions with reproducible evidence.
Communication: Clearly explain findings and tradeoffs to researchers, engineering partners, and executives.
Ways to Stand Out from the Crowd
Evaluation: Experience evaluating LLMs, multimodal models, or agentic systems through adversarial testing or automated red-teaming.
Community Contributions: Contributions to public benchmarks or evaluation frameworks.
Research Publications: A publication track record and interest in continuing to publish at top tier venues.
Human-Aligned Evaluation: Experience validating model-based evaluators against human judgments or designing human-in-the-loop evaluation workflows.
Statistical Rigor: Strong model evaluation skills and a commitment to sound statistical practices, including confidence intervals.
- Your base salary will be determined based on your location, experience, and other objective job-related factors. The expected base salary ranges for this position are:
- 184,000 USD – 287,500 USD for Level 4; and 224,000 USD – 356,500 USD for Level 5.
You will also be eligible for equity and benefits.
Applications for this job will be accepted at least until November 9, 2026.
This posting is for an existing vacancy.
NVIDIA uses AI tools in its recruiting processes.
NVIDIA is committed to fostering an inclusive work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.
