Staff Machine Learning Engineer – Production-Ready AI Systems

Bjak; Germany (OH); On-site; USD 140,000 - 190,000; Full time

Job summary

A1 is seeking a Member of Technical Staff, Machine Learning to build core ML components for real production systems. You will ship, debug, and iterate on models that run in production while collaborating with product and research teams.

You will focus on data, training, evaluation, and inference across production-grade ML pipelines, aiming for reliability, latency, and scalable performance in live environments.

Qualifications

• Strong foundations in machine learning and modern neural architectures.
• Some hands-on experience training, fine-tuning, or deploying ML models.
• Comfortable writing production-quality code and learning new tools quickly.
• Curious, coachable, and eager to learn from real systems in production.
• Able to work through ambiguity with guidance and grow ownership over time.
• Bias toward shipping, iteration, and continuous improvement.

Responsibilities

• Build and improve ML components across data, training, evaluation, and inference.
• Fine-tune and adapt models as part of larger production systems.
• Implement evaluation and testing to understand 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.
• Work closely with senior ML engineers and product teams.
• Work under real production constraints: latency, cost, reliability, and safety

Skills

ML fundamentals

Production ML

Strong coding

Collaboration

Learn quickly

Tools

Python

PyTorch

JAX

GPUs

Job description

A1 is seeking a Member of Technical Staff, Machine Learning to build core ML components for real production systems. You will ship, debug, and iterate on models that run in production while collaborating with product and research teams.

You will focus on data, training, evaluation, and inference across production-grade ML pipelines, aiming for reliability, latency, and scalable performance in live environments.