Machine Learning Engineer

As a Member of Technical Staff, Machine Learning, you will build core ML components. You will work on real production systems from day one, learning how large-scale ML behaves outside of research settings. This role is for engineers who want to develop strong systems judgment by shipping, debugging, and iterating on real-world ML. Overview

• 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

Tech Stack

• Python

• PyTorch / JAX

• Production ML systems running on GPUs

Ideal Experience

• 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.

Outcomes

• ML models in production meet expected accuracy, latency, and reliability targets.

• Production issues are identified quickly, debugged effectively, and root causes addressed.

• Data pipelines, training loops, and inference systems are robust, reproducible, and maintainable.

• Collaborates effectively with engineers, product, and research teams to deliver reliable ML-powered features.

• Iterations on models and systems are driven by real-world signals and measurable improvements.