Senior Generative ML Engineer - Build & Deploy Models

CyberCoders; Pittsburgh (Allegheny County); On-site; USD 140,000 - 190,000; Full time

Benefits offered by this job

Vacation/PTO

Medical

Dental

Vision

401k

Bonus

Job summary

CyberCoders seeks a Senior Machine Learning Engineer to lead the design, development, and deployment of advanced ML systems with a focus on generative modeling. You will build and optimize generative models, write production-grade Python code, and collaborate with cross-functional teams including researchers, software engineers, and product owners.

The role emphasizes production-ready pipelines, scalable infrastructure, and MLOps practices to ensure robust, maintainable solutions across multiple

Qualifications

• 5+ years of ML experience building production systems.
• Proficiency in Python and ML libraries.
• Experience with generative modeling (VAEs, GANs, diffusion).

Responsibilities

• Design and optimize generative models for real-world use.
• Produce production-grade Python code and reusable components.
• Lead end-to-end ML projects from data prep to deployment.
• Collaborate with researchers and domain experts.
• Build data pipelines and scalable infrastructure.
• Deploy and monitor models with MLOps.
• Tune performance and optimize inference latency.
• Mentor junior engineers and uphold reproducible research.
• Document experiments and deployment procedures.

Skills

Python

ML frameworks

Generative models

MLOps

Data pipelines

Tools

Docker

Kubernetes

Spark

Airflow

CI/CD

Job description

CyberCoders seeks a Senior Machine Learning Engineer to lead the design, development, and deployment of advanced ML systems with a focus on generative modeling. You will build and optimize generative models, write production-grade Python code, and collaborate with cross-functional teams including researchers, software engineers, and product owners.

The role emphasizes production-ready pipelines, scalable infrastructure, and MLOps practices to ensure robust, maintainable solutions across multiple