ML Systems Engineer, Production-Ready (Remote)

Protingent; Washington; Hybrid; USD 120,000 - 210,000; Full time

Job summary

Protingent Staffing seeks a Member of Technical Staff, Machine Learning for a fully remote role with our client. You will build core ML components and work on real production systems from day one, shipping and iterating on models in production.

You will collaborate with senior ML engineers and product teams to ensure models meet accuracy, latency, and reliability targets while managing data pipelines for real-world and synthetic data.

Qualifications

• Strong foundations in machine learning and modern neural architectures.
• Additional hands-on ML experience is desirable.
• Experience with production ML pipelines is a plus.

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.
• Ensure ML models in production meet accuracy, latency, and reliability targets.
• Identify, debug, and address production issues quickly with root-cause analysis.
• Ensure pipelines, training loops, and inference systems are robust and maintainable.
• Collaborate with engineers, product, and research teams to deliver ML-powered features.
• Drive iterations on models and systems using real-world signals.

Skills

ML fundamentals

Hands-on ML training

Production code

Learning agility

Ambiguity resilience

Shipping mindset

Python, PyTorch/JAX, GPUs

Job description

Protingent Staffing seeks a Member of Technical Staff, Machine Learning for a fully remote role with our client. You will build core ML components and work on real production systems from day one, shipping and iterating on models in production.

You will collaborate with senior ML engineers and product teams to ensure models meet accuracy, latency, and reliability targets while managing data pipelines for real-world and synthetic data.