Applied Scientist: Generative AI & Synthetic Data

Amazon; Sunnyvale (CA); On-site; USD 172,000 - 222,000; Full time

Benefits offered by this job

Health insurance

RSUs

401(k) matching

Paid time off

Parental leave

Job summary

Amazon Lab126 seeks an Applied Scientist to advance generative and simulation methods for synthetic data powering AI in Amazon devices. You will explore diffusion and multimodal models, 3D scene representations, and sim-to-real transfer in a hands-on, technically leading role.

You will mentor junior scientists, validate improvements on production models, and contribute to end-to-end production pipelines with a focus on cross-modal coherence and temporal alignment.

Qualifications

• PhD or Master’s degree with 4+ years of CS/CE/ML or related field experience
• Experience developing and implementing deep learning algorithms, particularly CV
• 2+ years building ML models for business applications
• Experience programming in Java, C++, Python or related language
• Experience in designing experiments and statistical analysis of results

Responsibilities

• Develop and fine-tune generative models and 3D simulation pipelines across video, audio, and sensor modalities.
• Develop training recipes that mix synthetic and real data and characterize when synthetic data helps or hurts.
• Build evaluation methods predicting synthetic data impact on downstream models.
• Model structured, rule-governed activity to keep data coherent across modalities and time.
• Validate work by measured improvements on production models and mentor junior scientists.

Skills

Deep learning

Computer vision

Machine learning for business

Java

C++

Python

Education

PhD

Master's degree in CS/CE/ML

Tools

MxNet

TensorFlow

Job description

Amazon Lab126 seeks an Applied Scientist to advance generative and simulation methods for synthetic data powering AI in Amazon devices. You will explore diffusion and multimodal models, 3D scene representations, and sim-to-real transfer in a hands-on, technically leading role.

You will mentor junior scientists, validate improvements on production models, and contribute to end-to-end production pipelines with a focus on cross-modal coherence and temporal alignment.