Machine Learning Lead Engineer (Fully Remote)

Next Step Systems; United States; Remote; USD 140,000 - 230,000; Full time

Benefits offered by this job

Medical insurance

Dental insurance

Vision insurance

Savings plan

PTO

Remote work

Job summary

Next Step Systems is seeking an experienced ML engineer to own and scale the end-to-end ML platforms powering our products, from data ingestion and training to evaluation, inference and deployment. You will build and evolve training and fine-tuning pipelines for large models, and architect high-performance inference systems that optimize latency, GPU utilization, memory and cost.

This fully remote role offers the chance to work on cutting-edge ML infrastructure and production-grade systems

Qualifications

• ML experience required, including building and shipping production ML systems.
• Strong understanding of large-model training, fine-tuning, evaluation, and inference.
• Strong software engineering and systems fundamentals, with experience operating GPU-based ML workloads at meaningful scale.
• Fully remote work environment.
• Opportunity to work on cutting-edge ML infrastructure and large models.

Responsibilities

• Own end-to-end ML systems powering the company, from data and training to evaluation, inference, and deployment.
• Build and evolve training and fine-tuning pipelines for large models.
• Architect high-performance inference systems, optimizing latency, GPU utilization, memory, cost, and reliability.

Skills

ML experience

Production ML

Software engineering

GPU workloads

Large models

Fine-tuning

Inference

Job description

• Own the end-to-end Machine Learning systems powering the company, from data and training to evaluation, inference, and deployment.
• Build and evolve training and fine-tuning pipelines for large models.
• Architect high-performance inference systems, optimizing latency, GPU utilization, memory, cost, and reliability.

Outcomes:

• Research and models reliably translate into production-ready solutions with clear performance and quality targets.
• ML pipelines, training loops, and inference systems are stable, efficient, and maintainable.
• Production issues are detected, debugged, and resolved quickly, minimizing user impact.

Qualifications:

• Machine Learning (ML) experience is required, including building and shipping production ML systems.
• Strong understanding of modern large-model training, fine-tuning, evaluation, and inference.
• Strong software engineering and systems fundamentals, with experience operating GPU-based ML workloads at meaningful scale.
• Medical insurance, Dental, Vision, Savings Plan Options, and PTO.
• Fully remote work environment.
• Opportunity to work on cutting-edge ML infrastructure and large models.