Principal Software Engineer, ML Flywheel Technical Lead (AI)

TL;DR

Principal Software Engineer, ML Flywheel Technical Lead (AI): Developing and integrating Foundation Model Teacher setups and data flywheel mining applications for autonomous driving with an accent on model distillation, evaluation infrastructure, and large-scale ML production. Focus on optimizing inference footprints, implementing robust evaluation metrics, and leading cross-functional engineering teams to enhance the hirify.global Driver.

Location: Hybrid; must be based in Mountain View, California or other US locations

Salary: $332,000—$421,000 USD

Company

hirify.global is an autonomous driving technology company with the mission to be the world's most trusted driver.

What you will do

• Integrate Foundation Model Teacher setups developed by AIF and ML Infra teams into customer student team frameworks.
• Collaborate with customers to understand their needs and develop technical solutions based on those requirements.
• Optimize the teacher/student model distillation process, model inference footprints, and sampling techniques to meet compute constraints.
• Define and implement the metrics and evaluation infrastructure necessary for evaluating hirify.global Foundation Models.
• Develop frameworks and approaches that power Data Flywheel mining and annotation applications using Foundation Models.

Requirements

• Machine learning expertise with a proven track record of contributing to large-scale ML production systems.
• Software engineering infrastructure experience and ability to analyze complex systems, algorithms, and modeling techniques.
• Experience evaluating and optimizing large-scale ML/AI systems.
• Experience leading engineering teams of 10+ people.
• Proven ability to hire and develop world-class managers and scientists.
• Strong communication, planning skills (e.g., building 12-month roadmaps), and cross-organizational collaboration skills.

Nice to have

• Direct experience developing large pre-trained foundational models such as LLM, VLM, or Video models.
• Experience across multiple ML applications (e.g., NLP and vision) and diverse settings (e.g., startup founder and FAANG leadership).
• Experience with the dev-ops side of ML models, including training latency and regression prevention.

Culture & Benefits

• Hybrid work schedule.
• Discretionary annual bonus program.
• Equity incentive plan.
• Generous company benefits program.