Lead ML Engineering—Scale Production AI

Axial Search; United States; On-site; USD 200,000 - 340,000; Full time

Job summary

Axial Search is seeking a Senior ML Engineering Leader to build and scale production ML infrastructure in the US. You will lead an engineering team, mentor engineers, and maintain technical credibility through hands-on contribution.

You will drive design decisions on pipelines, feature stores, deployment, and observability, partner with data scientists and product teams, and translate research into robust systems while delivering quarterly roadmaps and KPIs.

Qualifications

• 7+ years of ML engineering or infrastructure experience.
• At least 2 years in a management or technical leadership role.
• Proven track record shipping and operating production ML systems at scale.
• Strong ML fundamentals and proficiency in Python; hands-on with ML frameworks.
• Experience leading teams, mentoring, and managing performance.
• Familiarity with cloud ML platforms and containerization (Docker/Kubernetes).

Responsibilities

• Lead and grow an ML engineering team, owning hiring, mentorship, and career development while contributing hands-on.
• Own design and delivery of ML infrastructure, pipelines, and production systems.
• Drive technical roadmap decisions, balance build vs. buy, manage tech debt, align with business goals.
• Partner with data scientists and product teams to translate research into production systems.
• Establish best practices for reproducibility, versioning, testing, and observability.
• Own incident response and reliability for production ML workloads and post-mortems.
• Build quarterly roadmaps, track KPIs, and communicate ML impact to leadership.

Skills

ML engineering

Leadership

Python

ML frameworks

Docker

Kubernetes

Cloud platforms

System design

Hiring & mentorship

Tools

Docker

Kubernetes

AWS SageMaker

Vertex AI

Job description

Axial Search is seeking a Senior ML Engineering Leader to build and scale production ML infrastructure in the US. You will lead an engineering team, mentor engineers, and maintain technical credibility through hands-on contribution.

You will drive design decisions on pipelines, feature stores, deployment, and observability, partner with data scientists and product teams, and translate research into robust systems while delivering quarterly roadmaps and KPIs.