Remote Principal ML Engineer — Scale AI Systems

Energy Jobline ZR; San Francisco (CA); Remote; USD 180,000 - 240,000; Full time

Benefits offered by this job

Medical insurance

Dental

Vision

PTO

Savings Plan

Job summary

Energy Jobline ZR is seeking a Principal Machine Learning Engineer to architect and optimize large‑scale ML systems. This is a 100% remote role, guiding training, inference, evaluation, and infrastructure with a focus on scalable, reliable production deployments.

You will own end‑to‑end ML pipelines, including GPU‑based optimization and latency‑aware inference, while collaborating with backend, mobile, and desktop teams to ship impactful AI features.

Qualifications

• Strong background in deep learning and transformer‑based architectures.
• Hands‑on experience training, fine‑tuning, or deploying large‑scale ML models in production.
• Proficiency with modern ML frameworks (PyTorch, JAX) and ability to learn others quickly.
• Experience with distributed training and inference frameworks (DeepSpeed, Megatron, Ray).
• Strong software engineering fundamentals; production‑grade systems.

Responsibilities

• Architect and build large‑scale ML systems across data, training, evaluation, inference, and deployment.
• Design high‑performance training pipelines on GPU infrastructure.
• Architect inference systems balancing latency, throughput, cost and reliability.
• Own production deployment including GPU optimization and memory efficiency.
• Collaborate with engineering to integrate ML systems into products.

Skills

Deep learning

Transformer architectures

PyTorch

JAX

Distributed training/inference

GPU optimization

RLHF pipelines

Tools

DeepSpeed

Megatron

Ray

TensorRT

Job description

Energy Jobline ZR is seeking a Principal Machine Learning Engineer to architect and optimize large‑scale ML systems. This is a 100% remote role, guiding training, inference, evaluation, and infrastructure with a focus on scalable, reliable production deployments.

You will own end‑to‑end ML pipelines, including GPU‑based optimization and latency‑aware inference, while collaborating with backend, mobile, and desktop teams to ship impactful AI features.