Staff Machine Learning Engineer — Production Systems

Bjak; Town of Sweden (NY); On-site; USD 150,000 - 230,000; Full time

Job summary

A1 in the United States is seeking a Member of Technical Staff, Machine Learning to build core ML components for production systems from day one. You will work on real production systems, shipping, debugging, and iterating on ML with a focus on reliability and real-world performance.

You will collaborate with senior ML engineers and product teams to deliver robust ML-powered features, handling data, training, evaluation, and deployment in production 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.

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

Machine Learning

Python

PyTorch

Model deployment

Data pipelines

Tools

Docker

Kubernetes

Job description

A1 in the United States is seeking a Member of Technical Staff, Machine Learning to build core ML components for production systems from day one. You will work on real production systems, shipping, debugging, and iterating on ML with a focus on reliability and real-world performance.

You will collaborate with senior ML engineers and product teams to deliver robust ML-powered features, handling data, training, evaluation, and deployment in production environments.