Lead ML Systems Engineer — Production-Ready

Sarsfield Group; United States; Remote; USD 195,000 - 300,000; Full time

Benefits offered by this job

Health and Dental

Life insurance

401(k) matching

Equity

Job summary

Sarsfield Group in the United States is seeking a Lead Engineer, Machine Learning to own the execution layer of our intelligence, turning research and model capabilities into reliable, scalable production systems.

You will work across the model lifecycle: data, training, evaluation, inference, and deployment, in a hands-on leadership role at the intersection of research, systems, and product.

Qualifications

• Experience building and shipping ML systems used in production, not just research prototypes.
• Strong understanding of large-model training, fine-tuning, evaluation, and inference.
• Strong software engineering and systems fundamentals.

Responsibilities

• Own the end-to-end ML systems powering our company, from data and training to evaluation, inference, and deployment.
• Build and evolve training and fine-tuning pipelines for large models.
• Design evaluation systems that measure capability, robustness, safety, and real-world product performance.
• Architect high-performance inference systems, optimizing latency, GPU utilization, memory, cost, and reliability.
• Build data pipelines and systems for high-quality real-world and synthetic training data.
• Establish reliable production infrastructure for deploying, monitoring, and continuously improving models.
• Partner with research and application engineering to turn model capabilities into product improvements.

Skills

ML systems

GPU workloads

Production-grade code

System fundamentals

Experimentation

Decision making

Tools

Python

PyTorch / JAX

GPU-based training

Job description

Sarsfield Group in the United States is seeking a Lead Engineer, Machine Learning to own the execution layer of our intelligence, turning research and model capabilities into reliable, scalable production systems.

You will work across the model lifecycle: data, training, evaluation, inference, and deployment, in a hands-on leadership role at the intersection of research, systems, and product.