ML Lead: Build Scalable AI Models & Infrastructure

Ethereum Technologies LLC; Sunnyvale (CA); On-site; USD 180,000 - 270,000; Full time

Benefits offered by this job

Health insurance

401(k)

Job summary

Ethereum Technologies is seeking an experienced ML Lead in Sunnyvale, CA to design, develop, optimize, and deploy ML-based solutions. You will oversee end-to-end machine learning pipelines, including data preprocessing, model selection, training, inference, and implementation.

The role emphasizes strong leadership, collaboration with security/IT, and the deployment of scalable ML infrastructure across product platforms using PyTorch, TensorFlow, and related tools. Remote work is not specified.

Qualifications

• Strong programming in Python and R.
• Hands-on ML experience and predictive modeling.
• Proficiency with PyTorch and TensorFlow.
• Experience with deep learning, model deployment, and ML infrastructure.

Responsibilities

• Collect, preprocess, and integrate datasets from multiple sources.
• Develop predictive models for business use cases.
• Evaluate models, perform statistical analysis, and monitor deployed models.
• Design scalable ML infrastructure for data processing.
• Develop and deploy ML algorithms across product platforms.
• Collaborate with security and IT to ensure data privacy.
• Communicate model performance and project progress to stakeholders.
• Demonstrate leadership in problem solving and project management.

Tools

MLLib

PyTorch

TensorFlow

Python

R

Supervised Learning

Unsupervised Learning

Clustering

Dimensionality Reduction

Model Training

Model Inference

Model Optimization

KV Cache Optimization

Job description

Ethereum Technologies is seeking an experienced ML Lead in Sunnyvale, CA to design, develop, optimize, and deploy ML-based solutions. You will oversee end-to-end machine learning pipelines, including data preprocessing, model selection, training, inference, and implementation.

The role emphasizes strong leadership, collaboration with security/IT, and the deployment of scalable ML infrastructure across product platforms using PyTorch, TensorFlow, and related tools. Remote work is not specified.