# Machine Learning Engineer / AI Researcher / Data Scientist

**Company**: NVIDIA
**Location**: Santa Clara, CA
**Work arrangement**: remote
**Experience**: senior
**Job type**: full-time
**Salary**: 184,000 USD – 287,500 USD for Level 4; and 224,000 USD – 356,500 USD for Level 5
**Category**: Data Science
**Industry**: Technology

**Apply**: https://nvidia.wd5.myworkdayjobs.com/en-US/NVIDIAExternalCareerSite/job/US-CA-Santa-Clara/Senior-Machine-Learning-Engineer--AI-Safety-Evaluations_JR2029416?utm_source=yubhub.co&utm_medium=jobs_feed&utm_campaign=apply
**Canonical**: https://yubhub.co/job/machine-learning-engineer-ai-researcher-data-1fcc11bf-f1d/

## Description

NVIDIA is seeking a Machine Learning Engineer / AI Researcher / Data Scientist to join its AI Safety and Responsibility efforts within Enterprise Risk Management. The successful candidate will engage directly in engineering tasks, applying data science and statistics skills to establish evidence guiding the next generation of safer AI.

**Job Summary:** As an expert in machine learning for Evaluations, you’ll help establish the evidence that guides the next generation of safer AI. Your curiosity and scientific judgment can shape what we test, what we learn, and what we build next.

**Responsibilities:**

- Safety Benchmarking: Design and maintain safety benchmarks for large language and multimodal models, translating risk scenarios into test cases, datasets, and scoring rubrics.

- 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.

**Requirements:**

- 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.

- 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.

**Nice to Have:**

- 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.

**Salary:** 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.

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## Skills

### Required
- Python
- machine learning
- data science
- statistics
- benchmarking
- evaluation methodology
- reproducible evaluation
- automated red-teaming
- failure analysis

### Nice to have
- LLMs
- multimodal models
- adversarial testing
- human-aligned evaluation
- statistical rigor

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Source: [Apply at nvidia.wd5.myworkdayjobs.com](https://nvidia.wd5.myworkdayjobs.com/en-US/NVIDIAExternalCareerSite/job/US-CA-Santa-Clara/Senior-Machine-Learning-Engineer--AI-Safety-Evaluations_JR2029416?utm_source=yubhub.co&utm_medium=jobs_feed&utm_campaign=apply)