AI/ML Engineering Manager: Hands-On Manufacturing Leader

Socket.dev; Los Angeles (CA); On-site; USD 213,000 - 255,000; Full time

Benefits offered by this job

Medical, dental, vision, and life保险

401k

Relocation support

Flexible vacation policy

Equity

Job summary

Hadrian is seeking a people-first Engineering Manager to lead a team of ML engineering and full-stack data experts within Technical Data Intelligence. You will hire, grow, and mentor engineers, while staying close to technical work to guide direction and unblock the critical path.

You will own design reviews, set headcount strategy, and drive tooling for data annotation, active learning, and synthetic data augmentation, enabling production deployment of multimodal models for engineering data.

Qualifications

• 2-5+ years of engineering management experience leading machine learning engineering teams in production.
• Strong technical depth in ML, computer vision (detection/segmentation), multimodal image+text models, and/or data engineering.

Responsibilities

• Own the people leadership of your engineers: hiring, onboarding, mentorship, career growth, and performance.
• Stay technically hands-on as a player-coach: participate in design reviews and contribute directly when needed.
• Set technical direction and headcount investment with engineers and tech leads.
• Build and maintain annotation tooling, implement active learning loops, and engineer synthetic data augmentation strategies.
• Ensure engineers are engaged, growing, and set up to do their best work.

Skills

Engineering management

Machine learning

Computer vision

Multimodal ML

Data engineering

Job description

Hadrian is seeking a people-first Engineering Manager to lead a team of ML engineering and full-stack data experts within Technical Data Intelligence. You will hire, grow, and mentor engineers, while staying close to technical work to guide direction and unblock the critical path.

You will own design reviews, set headcount strategy, and drive tooling for data annotation, active learning, and synthetic data augmentation, enabling production deployment of multimodal models for engineering data.