Lead ML Engineer: Scale Production AI Systems

Salt Digital Recruitment; United States; On-site; USD 180,000 - 260,000; Full time

Job summary

Salt Digital Recruitment is seeking a Lead Engineer - Machine Learning to own the production ML systems lifecycle, from data handling and training to inference and deployment. You will design scalable training pipelines and robust evaluation frameworks, balancing performance, reliability and cost while collaborating with research and app engineering teams.

The role requires deep expertise in modern large-model workflows, strong software fundamentals, and a track record of shipping production ML

Qualifications

• Experience shipping machine learning systems to production.
• Strong understanding of large-model training, fine-tuning, evaluation and inference.
• Ability to design and optimize production ML infrastructure.

Responsibilities

• Own end-to-end ML systems from data to production deployment.
• Build and evolve training and fine-tuning pipelines for large models.
• Design evaluation systems for model capability, safety and real-world performance.
• Architect high-performance inference systems with optimized latency and resource use.
• Establish reliable production infrastructure for deploying and monitoring models.
• Collaborate with research and engineering to translate model capabilities into product improvements.
• Diagnose and resolve production model and system issues.
• Make pragmatic technical trade-offs and iterate based on real-world metrics.
• Provide technical leadership to engineers across ML systems.

Skills

ML systems

GPU training

Python

PyTorch

JAX

Distributed ML

Model evaluation

Production ML

Tools

PyTorch

JAX

Job description

Salt Digital Recruitment is seeking a Lead Engineer - Machine Learning to own the production ML systems lifecycle, from data handling and training to inference and deployment. You will design scalable training pipelines and robust evaluation frameworks, balancing performance, reliability and cost while collaborating with research and app engineering teams.

The role requires deep expertise in modern large-model workflows, strong software fundamentals, and a track record of shipping production ML