Director of ML Engineering & Production Systems

Axial Search; United States; On-site; USD 150,000 - 280,000; Full time

Job summary

Axial Search is seeking an experienced ML Engineering Lead in the United States to lead a team of ML engineers and data scientists. You will own the end-to-end ML pipeline—from data ingestion to deployment—ensuring latency and accuracy in production, and collaborating with product to drive measurable impact.

You will mentor engineers, set best practices, and guide infrastructure decisions that reduce friction in the ML lifecycle.

Qualifications

• 5+ years in ML engineering, MLOps, or data engineering.
• 2+ years in a leadership or senior IC role.
• Proven track record shipping ML models in production.
• Experience with model deployment, monitoring, and troubleshooting in live environments.
• Ability to hire, unblock engineers, and raise technical bar.

Responsibilities

• Lead and grow an ML engineering team, hiring for technical depth and problem-solving mindset.
• Own the end-to-end ML pipeline—data ingestion, model training, validation, and deployment.
• Partner with data science and data engineering teams to translate model research into scalable systems.
• Drive infrastructure and tooling decisions that reduce friction in model development, testing, and deployment cycles.
• Work with product and business stakeholders to define success metrics and align ML initiatives with priorities.
• Build and maintain documentation, best practices, and playbooks for monitoring, retraining, and incident response.
• Identify and address technical debt and operational bottlenecks to ensure reliability.

Skills

Python

Git

Cloud platforms

Containerization

Orchestration frameworks

Tools

Kubernetes

Job description

Axial Search is seeking an experienced ML Engineering Lead in the United States to lead a team of ML engineers and data scientists. You will own the end-to-end ML pipeline—from data ingestion to deployment—ensuring latency and accuracy in production, and collaborating with product to drive measurable impact.

You will mentor engineers, set best practices, and guide infrastructure decisions that reduce friction in the ML lifecycle.