Staff Machine Learning Engineer — Production ML & Systems

Embedded Shishya; United States; On-site; USD 140,000 - 190,000; Full time

Job summary

ActAI is seeking a Member of Technical Staff, Machine Learning to build core ML components in production systems from day one. You will work on data, training, evaluation, and inference across real-world workflows, iterating on models under production constraints.

The role emphasizes shipping reliable ML features, debugging production issues, and collaborating with senior ML engineers and product teams. A strong ML foundation and hands-on deployment experience are essential.

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.

Skills

ML foundations

Neural architectures

Production ML

Python

Learning quickly

Shipping mindset

Tools

Python

PyTorch / JAX

GPUs

Job description

ActAI is seeking a Member of Technical Staff, Machine Learning to build core ML components in production systems from day one. You will work on data, training, evaluation, and inference across real-world workflows, iterating on models under production constraints.

The role emphasizes shipping reliable ML features, debugging production issues, and collaborating with senior ML engineers and product teams. A strong ML foundation and hands-on deployment experience are essential.