ML Engineer (Foundation Model Infrastructure)

TL;DR

ML Engineer (Foundation Model Infrastructure): Build and operate petabyte-scale data systems and ML pipelines at the heart of hirify.global's foundation model development with an accent on shepherding research prototypes to robust production components. Focus on creating automated infrastructure for benchmarking, monitoring, and safe model release, wielding large-scale compute with JAX and Flume to drive end-to-end ML lifecycle improvements.

Hybrid work schedule, onsite in Mountain View, California

Salary Range $204,000—$259,000 USD

Company

Autonomous driving technology company building the hirify.global Driver for ride-hail service and vehicle platforms, with over 100 million autonomous miles driven.

What you will do

• Build and operate petabyte-scale data systems and ML pipelines for foundation model development
• Shepherd cutting-edge foundation models from research prototypes to robust hirify.global Driver components
• Create automated infrastructure for benchmarking, monitoring, and safely releasing models
• Wield large-scale compute and frameworks like Flume and JAX to process datasets and train/deploy models
• Drive improvements in speed, reliability, and efficiency of the ML development lifecycle
• Partner with AI Foundations, ML, and Platform experts to transform innovations into on-road improvements

Requirements

• Masters degree in Computer Science, Machine Learning, Robotics, or equivalent practical experience
• Proficiency in Python
• Proficiency in C++
• Familiarity with modern deep learning frameworks (e.g., Pytorch, JAX, Tensorflow)
• Experience building or maintaining large-scale data pipelines or ML infrastructure (e.g., Flume, Spark, Borg, Kubeflow)

Nice to have

• Strong hands-on SWE skills for large, complex shared codebases
• Experience in AV planning and related research
• Experience designing distributed systems or MLOps platforms (model versioning, experiment tracking, CI/CD for ML)
• Prior work developing ML model evaluation methodologies in industrial or research settings

Culture & Benefits

• Hybrid work schedule reporting to a Senior Research Scientist
• Discretionary annual bonus program
• Equity incentive plan
• Generous company benefits program