ML Evaluation & Metrics Engineer for AI Safety

NVIDIA; New York (NY); Presencial; USD 152 000 - 242 000; Tempo integral

Vantagens oferecidas por esta oferta de emprego

Equity

Generous benefits package

Resumo da oferta

NVIDIA in New York is seeking an Evaluation/ML-Systems Engineer to own how we measure the program. You will design the metrics, baselines, and protocols that make comparisons fair, and build infrastructure that ties every result to its run and code.

You will partner with security researchers and platform engineers to design verifiable experiments. The role emphasizes evidence-based evaluation, reproducibility, and scalable measurement in a fast-moving AI security program.

Qualificações

• Bachelor's degree (or equivalent experience) with 5+ years in ML engineering or evaluation.
• Evaluation experience: Designing benchmarks, metrics, and statistically sound comparisons for ML systems.
• Measurement rigor: A careful, skeptical approach to metrics, baselines, and claims.
• Engineering skills: Solid Python engineering for shared infrastructure, including experiment tracking and data pipelines.

Responsabilidades

• Evaluation infrastructure: Build the benchmarking and reproducibility systems we depend on.
• Metrics and protocols: Define the metrics and protocols we measure against.
• Traceability: Map every result to the code and runs that produced it.
• Evidence discipline: Keep findings reviewable and conclusions traceable.

Conhecimentos

ML evaluation

Python engineering

Experiment tracking

Data pipelines

Statistical analysis

Formação académica

Bachelor's degree or equivalent experience

Ferramentas

Python

Descrição da oferta de emprego

NVIDIA in New York is seeking an Evaluation/ML-Systems Engineer to own how we measure the program. You will design the metrics, baselines, and protocols that make comparisons fair, and build infrastructure that ties every result to its run and code.

You will partner with security researchers and platform engineers to design verifiable experiments. The role emphasizes evidence-based evaluation, reproducibility, and scalable measurement in a fast-moving AI security program.