Principal ML Engineer - Remote, Scalable AI Systems

Ginas Tech Jobs; San Francisco (CA); Remote; USD 170,000 - 200,000; Full time

Benefits offered by this job

Medical insurance

Dental

Vision

Savings plan

Paid time off

Job summary

Ginas Tech Jobs is seeking a Principal Machine Learning Engineer to set the technical standard for ML systems across training, inference, evaluation and deployment. This 100% remote role balances hands‑on engineering with architectural leadership, spanning data, models, and production pipelines.

You will own large‑scale ML systems, optimize GPU memory and latency, collaborate with backend/mobile teams, and guide research‑to‑production cycles.

Qualifications

• Strong background in deep learning and transformer‑based architectures.
• Artificial Intelligence (AI) experience required.
• Hands‑on experience training, fine‑tuning, or deploying large‑scale ML models in production.
• Proficiency with at least one modern ML framework (e.g. PyTorch, JAX), and ability to learn others quickly.

Responsibilities

• Architect and build large‑scale ML systems spanning data, training, evaluation, inference, and deployment.
• Architect inference systems that balance latency, throughput, cost, and reliability at scale.
• Design and maintain data systems for high‑quality synthetic and real‑world training data.
• Own production deployment, including GPU optimization, memory efficiency, latency reduction, and scaling policies.
• Collaborate closely with application engineering to integrate ML systems cleanly into backend, mobile, and desktop products.

Skills

Deep learning

Transformer models

AI experience

Production ML

Tools

PyTorch

JAX

DeepSpeed

Megatron

Ray

Job description

Ginas Tech Jobs is seeking a Principal Machine Learning Engineer to set the technical standard for ML systems across training, inference, evaluation and deployment. This 100% remote role balances hands‑on engineering with architectural leadership, spanning data, models, and production pipelines.

You will own large‑scale ML systems, optimize GPU memory and latency, collaborate with backend/mobile teams, and guide research‑to‑production cycles.