ML Engineer: Auto-Labeling & Active Learning

Avala; San Francisco (CA); Hybrid; USD 150,000 - 230,000; Full time

Benefits offered by this job

Equity

Health insurance

Unlimited PTO

FSA

Job summary

Avala is seeking an ML Engineer to build and refine the labeling platform that powers its agentic annotation system. You will work at the intersection of foundation models, active learning, and production annotation workflows.

The role involves developing auto-labeling models (2D, 3D, segmentation), routing tasks between AI and human annotators, and creating feedback loops to steadily improve accuracy and coverage.

Qualifications

• 3+ years training and deploying ML models in production.
• Experience with object detection, segmentation, or 3D perception models.
• Proficiency in Python, PyTorch, and distributed training frameworks.
• Experience with active learning, semi-supervised learning, or human-in-the-loop ML.
• Understanding of ML ops — model versioning, A/B testing, monitoring.

Responsibilities

• Develop and fine-tune foundation models for auto-labeling.
• Build active learning systems routing tasks between AI and humans.
• Design feedback loops using human corrections to improve models.
• Implement model evaluation pipelines for annotation quality and coverage.
• Scale inference infrastructure for real-time auto-labeling across sensors.
• Collaborate with operations to optimize the human-AI workflow.

Skills

Object detection

3D perception

Python

Active learning

MLOps

Distributed training

Tools

PyTorch

Job description

Avala is seeking an ML Engineer to build and refine the labeling platform that powers its agentic annotation system. You will work at the intersection of foundation models, active learning, and production annotation workflows.

The role involves developing auto-labeling models (2D, 3D, segmentation), routing tasks between AI and human annotators, and creating feedback loops to steadily improve accuracy and coverage.