Staff ML Engineer — Production-Driven Systems

Hidden Jobs; United States; Remote; USD 120,000 - 180,000; Full time

Job summary

Hidden Jobs is seeking a Member of Technical Staff, Machine Learning, for an AI-focused engineering team to build core ML components for production AI products. This role suits engineers who want to ship, debug, and iterate on real-world ML from day one.

You will develop ML components across data, training, evaluation, and inference; fine‑tune models for production systems; implement evaluation methods; help maintain data pipelines; and address production incidents as you ship updates and learn

Qualifications

• Strong foundations in machine learning and modern neural architectures.
• Hands-on experience training, fine-tuning, or deploying ML models.
• Comfortable writing production-quality code and learning new tools quickly.
• Working knowledge of Python and either PyTorch or JAX.
• Curious, coachable, and eager to learn from real production systems.
• Able to work through ambiguity with guidance and grow ownership over time.

Responsibilities

• Build and improve ML components spanning data, training, evaluation, and inference.
• Fine-tune and adapt models as part of larger production systems.
• Implement evaluation and testing approaches that explain model behavior.
• Help build and maintain data pipelines for real-world and synthetic data.
• Debug model issues, performance problems, and production incidents.
• Ship improvements iteratively and learn from real user feedback.

Skills

Machine Learning fundamentals

Deep learning architectures

Model deployment

Systems thinking

Tools

Python

PyTorch

JAX

Job description

Hidden Jobs is seeking a Member of Technical Staff, Machine Learning, for an AI-focused engineering team to build core ML components for production AI products. This role suits engineers who want to ship, debug, and iterate on real-world ML from day one.

You will develop ML components across data, training, evaluation, and inference; fine‑tune models for production systems; implement evaluation methods; help maintain data pipelines; and address production incidents as you ship updates and learn