Member of Technical Staff, Machine Learning

Role overview

Develop and improve machine learning components used in production, gaining experience across data, training, evaluation, and inference. The work combines model adaptation and experimentation with practical engineering: diagnosing issues, meeting operational constraints, and iterating based on evidence from real use.

Responsibilities

- Build and improve ML components across data processing, training, evaluation, and inference. - Fine-tune or adapt models for use within larger production systems. - Implement tests and evaluations to understand model behavior and track improvements. - Help create and maintain pipelines for real-world and synthetic data. - Investigate model quality issues, performance problems, and production incidents, addressing root causes. - Deliver iterative improvements in collaboration with experienced ML engineers and product teams.

Requirements

- Strong foundations in machine learning and modern neural network architectures. - Some hands-on experience training, fine-tuning, or deploying models. - Ability to write production-quality code and learn tools quickly. - Curiosity and openness to feedback while learning from deployed systems. - Ability to handle ambiguous problems with guidance and take on more ownership over time. - Interest in shipping, iteration, and measurable improvement.

Nice to have

- Familiarity with Python, PyTorch or JAX, and GPU-based machine learning systems. - Experience working with latency, cost, reliability, or safety constraints in production.